5 4 1. Introduction In order to survive and gain competitive advantage in this volatile global environment, organizational leaders need to understand not only the drivers of superior organizational performance (individual risk), but also the drivers of superior macroeconomic performance (market risk). The success and subsequent growth of a firm can be measured in various ways. Internal growth is a key measure of company growth. This success is also reflected by its stock market quotes. However, company growth is not only due to internal growth, depending also on general macroeconomic conditions, which will impact investor s decision of investment, and this also determines the future company performance. Previous authors studies allow us to state that the stock market and the overall economy are significantly related. As such, the role of macroeconomic variables in asset pricing theories is accepted to be important. Macroeconomic variables fluctuations affect business negatively by disturbing the smoothness of trade. There have been many attempts empirically performed in order to identify the link between macroeconomic variables and stock market volatility. Recently, Akbar et al. (2012) stated that it has been popular to study the relationship between macroeconomic growth and stock market performance. Stock markets are mainly affected by the surrounding economy and useful to predict future economic conditions (Fama, 1990; Binswanger, 2000). Every country and stock exchange market has unique determinants specific to itself. Therefore, for the same considered variables, they may have different responses. So, estimation of future trends of macroeconomic variables can be helpful to see the leading direction of stock returns. This study aims to improve investors understanding and evaluation of the relevant stock returns to the systematic influences of macroeconomic factors including the stock market index, inflation rate, interest rate, consumer confidence, industrial production and oil price shocks. The derived information about the relationship between macroeconomic variables and stock market performance can enable investors to make optimal decision in their global business investments. We have chosen to work with data from 2 European countries (UK and Germany) and three world developed countries (USA, Australia and Japan) on the basis of monthly returns for the period between March 1993 and February For all this countries we have considered individual company data as well as their sector stocks representatives. We chose these countries because developed countries financial markets are observed to be more explained compared to the other financial markets. Results can be summarized as follows: 1) The market index revealed to be the variable which most influences both companies and sector stock index returns for all the countries under study; 2) There is no linear evident relationship between the rest of the macroeconomic variables and stock returns; 3) Results cannot be generalized in terms of sectors or countries; 4) Results suggest the importance of the inclusion of the consumer confidence index as an important affecting macroeconomic variable over returns; 5) Inflation, industrial production, market index and oil prices have a positive and statistically significant effect over developed sector stock market index returns; 6) It is shown that insignificant beta coefficients estimates obtained are not due to a bad choice of repressors, but yes to the instability of beta coefficients estimates throughout time which leaves room for future work. It is expected that the findings of this study would provide meaningful insights to the body of literature, policy makers as well as the practitioners. The results of this study are expected to support the theoretical framework of the determinants of stock market movement from the developing economies perspective. Moreover, the identification and weight of the magnitude of macroeconomic variables effects over financial markets will impact both company and public policies, which interest scholars, investors and companies. The rest of the work develops as follows. Section 2 provides a brief literature review, where section 3 presents the methodology and data used for the empirical results to be discussed in section 4 and in

6 light of the attained results, main implications are discussed in this section. Finally, section 5 concludes. 2. Literature Review Previous literature reports that stock prices in well-developed markets are influenced by changes in macroeconomic variables. While Sharpe (2002) got a negative relation between expected long-term earnings growth and expected inflation, Jones and Wilson (2006) observed that inflation adjustments can weakly estimate stock returns. There is a general consensus among scholars that stock market plays an important role in the development of an economy (Levine and Zervos, 1998; Sharpe, 2002; Jones and Wilson, 2006; Adjasi and Biekpe, 2006; Hearn and Piesse, 2010). It has been proved that capital markets accelerate economic growth by enhancing mobilization of domestic and foreign resources and facilitating investment (Bencivenga et al., 1996). Capital markets also provide an avenue for growth oriented companies to raise capital at low cost (Marone, 2003), reduces reliance on bank finance which is susceptible to interest rate fluctuations, and provides a channel for foreign capital inflows (Yartey, 2008). However, it can also be state that these capital markets provide an opportunity for venture capital firms to exit and liquidate their investments in domestic start-up ventures (Black and Gilson, 1999). There have also been some critics of securities market, arguing that markets characterized by weak corporate control mechanisms may jeopardize investor wealth (Khanna, 2009; La Porta et al., 1998; 1997), even worse for foreign investors (World Bank, 2005) who are likely to dispose their shares at discount prices. As stated by Hearn and Piesse (2010), this phenomenon is more pervasive in developing economies because they are characterized by weak regulatory institutions and poor systems of corporate governance. We are able to find many literature attempts to study the relation between stock markets and macroeconomic variables. Gunasekarager et al. (2004) choose the Sri Lanka stock market and money supply, Treasury bill rate, CPI and exchange rates as macroeconomic variables. The authors observed that all macroeconomic variables especially Treasury bill rate had a significant influence on stock prices except the exchange rate. However, the share price index could not be found to have influence on macroeconomic variables except the Treasury bill rate. For the Pakistani market, Nishat and Shaheen (2004) took the data from 1973 to 2004 and by considering industrial production index, the consumer price index, money supply, the value of an investment earnings and the money market rate, they employ the unit root test, Augmented Dickey Fuller (ADF) test, vector error correction model (VECM) and Granger-causality in order to determine the relationship among the considered variables. They found that industrial production is the largest positive and inflation is the largest negative influencing factors of stock prices. There was also a reverse causality observed between industrial production and stock prices. For the Athens stock exchange, Patra and Poshakwale (2006) observed both short and long-term relationships between inflation, money supply and trading volumes but no relationship between the exchange rate and stock prices. There is also evidence of none impact of macroeconomic variables over stock returns. Kandir (2008) uses monthly data from July 1997 to June 2005 and the multiple regression model to suggest a negative impact of interest rates on stock returns, since interest rate was the best alternative investment opportunity in Turkey. Conclusions point out that industrial production, money supply and oil prices don t show any significant influence on stock returns. But a significant effect of the exchange rate was identified. Özlen and Ergun (2012) study macroeconomic variables (inflation rate, exchange rate, interest rate, current account deficit and unemployment rate) and their effects on stock returns of 45 companies from 11 different sectors in Turkey. The Autoregressive distributed lag method is employed for the monthly data spanning from February, 2005 to May, The overall results indicate that exchange rate and interest rate are the most significant factors in stock price fluctuations of companies. Gay and Nova (2008) used Augmented Dickey-Fuller (ADF) test on exchange rate and oil price for Brazil, Russia, India, and China (BRIC) and the monthly data of stock market indices between 1999 and The relationship between exchange rate and oil price over the stock market index prices for the countries analyzed was not significant. 5

7 6 Using ten years of data, from June 1998 to June 2008, Hasan and Javed (2009) evaluated macroeconomic 1 variables and equity prices by using several linear time series models. Oil prices and inflation are detected as no significant but interest rate (IR), exchange rate and money supply appeared to be significant in the long run. Furthermore, the error correction model (ECM) has captured the short term dynamics of prices effect on equity prices. Sohail and Hussain (2009) found out that there is both a long-run and short-run relationship between macroeconomic variables and stock returns in the Lahore stock exchange from December 2002 to June 2008, finding also that inflation negatively influences stock returns while there are positive influences of money supply, industrial production and real effective exchange rate on stock prices. According to Rjoub et al. (2009) empirical analysis, it seems to exist a relationship between macroeconomic variables including interest rate, unanticipated inflation, risk premium, exchange rate, money supply, unemployment rate and the Istanbul Stock Market (ISE) from January 2001 to September 2005 by using the arbitrage pricing theory (APT) model, correlation among explanatory variables and portfolios regression. A significant pricing relationship between the stock return was identified. Moreover, macroeconomic variables are found to have a significant influence on the stock market returns in various portfolios. On the other hand, results suggested that there should be other macroeconomic factors affecting stock market returns in Istanbul Stock Market (ISE) instead of the tested ones due to the existence of weak explanatory power of the selected variables. For this same market, Gencturk (2009) studied the relations between stocks in Istanbul Stock Exchange (ISE) and macroeconomic variables by considering crisis periods and normal periods. Therefore, ISE-100 index is taken as the dependent variable; and treasury bond interest rates, consumer price index, money supply, industrial production index, dollar, gold prices are taken as independent variables. Also, Aktas (2011) studied the influence of 19 macroeconomic announcements on equity index options for the period from 1983 to 2002 in ISE and found out that balance of trade, consumer price index, producer price index; employment, housing starts, money supply and retail sales are strongly related with index option returns. By applying the Structural VAR (SVAR) model, Akay and Nargeleçekenler (2009) studied the relationship between monetary policy, inflation rate, industrial production index, interest rates and stock prices. A negative monetary shock was observed to be influential on the interest rate in both the long and short term. Consequently, it negatively affects stock prices. Sayılgan and Süslü (2011) analyzed the influence of macroeconomic factors on stock returns in emerging market economies using panel data from 1996 to Stock returns are found to be significantly influenced by exchange rates, inflation rates and the S&P 500 Index while returns are not influenced by interest rate, gross domestic product, money supply and oil prices. Hosseini et al. (2011) studied the relationships between stock market indices and four macroeconomics variables including crude oil price, money supply, industrial production and inflation rate in China and India for the period January 1999 to January Results suggest both long and short run linkages between macroeconomic variables and the stock market index in both countries. One year after, Quadir (2012) investigates the effects of macroeconomic variables like Treasury bill interest rate and industrial production on stock returns on Dhaka Stock Exchange for the period between January 2000 and February 2007 on the basis of monthly time series data using the Autoregressive Integrated Moving Average (ARIMA) model. The paper takes the overall market stock returns as an independent variable, and does not consider the stock returns of different companies separately. Although results indicate a positive relationship between Treasury bill interest rate and industrial production with market stock returns, the coefficients have turned out to be statistically insignificant. Although considerable attention has been devoted to the relationship between stock markets and economic growth, there is still little empirical work on the determinants of stock market development in developing economies as well as the determinants of individual company stocks. Moreover, country specific data usage is necessary to determine the relationship between 1 These include inflation, industrial production, oil prices, short term interest rate, exchange rates, foreign portfolio investment and money supply.

8 macroeconomic factors and securities market development because it is presumed that the determinants of securities market development vary from country to country depending on nature of regulatory mechanisms, economic policies, as well as institutional structures. Tangjitprom (2012) reviews a number of studies on macroeconomic factors and stock returns. All of the macroeconomic variables are classified into four groups: variables reflecting general economic conditions, variables related to interest rate and monetary policy, variables concerning price level, and variables concerning international activities. Furthermore, various studies on macroeconomics factors and stock returns have employed different methodologies based on their purposes and interpretations. Although the results are mixed, most studies have shown evidence that there are significant relationships between macroeconomic variables and stock returns. Zhu (2012) study the impact of macroeconomic factors on returns of the energy sector in Shanghai stock market (SEE), which are inflation rate, money supply (M2), exchange rate, industrial production, bond, exports, imports, foreign reserve and unemployment rate. The findings reveal that exchange rate, exports, foreign reserve and unemployment rate have effects on the stock return of energy sector in Shanghai stock market. Benaković and Posedel (2010) analyzes returns on fourteen stocks of the Croatian capital market in the period from January 2004 to October 2009 using inflation, industrial production, interest rates, market index and oil prices as factors. The analyses included fourteen stocks and their sensitivities to factors were estimated. The results show that the market index has the largest statistical significance for all stocks and a positive relation to returns. Interest rates, oil prices and industrial production also marked a positive relation to returns, while inflation had a negative influence. This brief literature review highlights that macroeconomic factors are critical in predicting the variability of stock returns. The key macroeconomic factors in the prediction of the stock returns may be price volatility of energy, interest rate risk, money supply, risk free rate, exchange rates, inflation and industrial production index. The review of the literature has presented that there are many studies which consider the micro (company size and dividend yield) and macro factors together. There may be other influencing factors such as the transmission of shocks and psychological effects (the consumer confidence index could be used) in the determination of stock price movements. They may include the changes in world oil prices, changes in interest rates and inflation rates. No standardized set of macroeconomic variables exist, despite the clear relationship between stock market and economic activities. However, inflation rate, exchange rate, interest rate, and unemployment rate are the most popular significant factors in order to explain stock market movements. The present study differs from the previous ones by taking both sectored and company data into account. 3. Methodology and Data The present study uses factor models based on the arbitrage pricing theory (APT) which allow us to include several factors into one regression which may influence stock returns. In this model, it is need to use previous statistical and economic analysis to determine which factors must be used as independent variables. The econometric model used obeys the following specification: r i, t i i,1f1, t i,2f2, t... i, k Fk, t i, t, (1) where r i,t represents the return on stock i computed as the log difference between consecutive prices, α is the constant term, β i measures the sensitivity of a stock I to a set of n macroeconomic factors, F n indicates realizations of macroeconomic factors and ε is the error term with an expected value of zero. In this work we have collected daily data from both sectors and 5 different individual companies for each of the considered ten sectors for countries like UK, US, Australia, Japan and Germany from the 7

9 8 period of March 1993 until February So, in total we have collected data for 250 individual companies and 50 sector indices. The daily data was converted into monthly returns using the month last day of trading available data. The five companies considered for each sector were randomly selected where we have decided to collect from those companies for which we had more years of available data over the sample available. Data for the considered representative sector indices goes from December 1992 until October 2012, which were also converted into monthly series. The sectors here analyzed are Basic Materials (BM), Consumer Goods (CG), Consumer Services (CS), Financials and Banks (F; B), Healthcare (HC), Industrials (I), Oil and Gas (OG), Technology (Tec), Telecommunications (Tel) and Utilities (U). When analyzing sector market indices the world index for that specific sector has been considered as the proxy for the market index. As stated previously, in the multifactor model it is the researcher who identifies which and how many factors are to be identified in regressions. The factors used in the present analysis were selected based on the literature review previously presented and which were considered to be the most representative. With respect to the number of factors Campbell, Lo and MacKinlay (1997) show that it is sufficient to use between three to six factors in the model. Having this in mind, and for the US capital market, Chen, Roll and Ross (1986) use as macroeconomic variables industrial production, inflation, risk premium, term structure, market index, consumption and oil prices. Macroeconomic variables used in the present work, can be classified into five groups of variables: reflecting general economic conditions, related to interest rate and monetary policy, concerning price level, considering investors behavior and concerning international activities. As such, we have used oil returns, the inflation rate, the industrial production index, the market interest rate, the market index and the consumer confidence index for each of the countries under analysis. So, each series is the country specific series and data refers to monthly observations. All price series have been converted into log returns and were denominated in US dollars. All collected data is from DataStream. Consistent with previous studies inflation (π in tables) has been used as a measure of macroeconomic stability (Nacuer et al., 2007). Macroeconomic stability has been found to exert effects on stock market development and inflation, which consists in a rise in the general price level, reduces the real value of money and as a consequence the expected cash inflow of an asset. So, we expect inflation to affect negatively both stock and sector indices. For some countries we use the direct inflation rate provided by the data, while for others the consumer price index of the respective country was used to compute it. Nacuer et al., (2007) found that macroeconomic instability has a negative and significant relationship with stock market capitalization. Boyd et al., (2001) found a nonlinear relationship between inflation and equity market development such that as inflation rises, the marginal impact on stock market development diminishes rapidly. In Yartey (2008) no significant relationship between inflation and stock market development was found. Kolluri and Wahab (2008) use an asymmetric model to examine the relationship between expected inflation and stock returns. They found that a negative relationship exists during low inflation regimes whereas there is a positive relationship during high inflation regimes. Research on the relationship between financial sector development and economic growth is not consensual. For some, the banking sector development has a positive effect on economic growth (Christopoulos and Tsionas, 2004) while others suggest that the banking sector may not be beneficial for growth (Singh, 1997). In reality, the banking sector is important for the stock market development because it affords investors with liquidity by advancing credit, and facilitating savings. Nacuer et al., (2007) and Yartey (2008) found support to a positive relationship between banking sector development and stock market development. The last, found that a very high level of bank sector development may have negative effects because stock markets and banks tend to substitute one another as financing sources. Stock markets and banks are considered as competitors in providing finance. So, with a well-developed money market, the capital market may be overshadowed leading to a slower rate of development. Our measure of the banking sector is countries interest rates (IR in the tables). We know that high interest rates tend to decrease the

10 present value of future cash flows. This will reduce the investment attractiveness and according to economic theory this will reduce stock prices. The interest rate used here is the 3-month Treasury bill reference interest rate, one for each respective country. As such, we expect a negative sign between interest rates and stock returns. Li, Iscan, and Xu (2010) use the US s Federal fund rate and Canada s overnight rate to study the effect of policy shock on stock prices. Stock returns are affected by monetary policy shocks in both countries, being this effect in the US more pronounced. They have addressed this difference in relation to the level of stock market openness. Chang et al. (2011) conducted a study about monetary policy and stock returns using the Federal fund rate. They found that the Federal fund rate had little effect on stock returns. Some studies have used other interest rates, such as LIBOR or LIBOR futures, to be the proxy of change in interest rate level. Gregoriou et al. (2009) used 3-month Sterling LIBOR futures as the proxy for monetary policy shocks in the UK market, finding a negative relationship between interest rate changes and stock returns. Another variable used as a proxy for economic conditions and activity in previous studies is the industrial production index. Here we use the industrial production (IP in the tables) growth rate for each respective country. Humpe and Macmillan (2009) used co-integration analysis to show a positive relationship between the industrial production index and stock prices in both the US and Japanese market. The authors emphasize that the industrial production index can be a better proxy for economic conditions in general. Also Benakovic and Posedel (2010) use the industrial production index given that GDP data is only published on a quarterly basis. It is expected a positive relationship between industrial production and stock returns. Globalization turned international activities important. Exchange rate is one of the most important factors in this group, especially for the countries that depend to a great extent on international trading activities as the ones we are analyzing here. Maysami, Howe, and Hamzah (2004) have studied the relationship between stock returns and macroeconomic variables, including exchange rate in Singapore. They found a significantly positive relationship, which can intuitively be explained by the high level of international trade of Singapore. In the present study we do not use exchange rates as independent variables directly but in a disguised way. Exchange rates are used to transform all the needed series into dollar prices. Another independent variable considered in the present study is oil prices. The oil price (OP in the tables) series used here is the West Texas Intermediate (WTI), monthly spot price. Some studies have focused on oil prices, which are a critical asset in both the production and consumption process, also considered as a proxy for cost-push inflation. This importance is due to the fact that oil price rises increase the uncertainty in capital markets and the risk of inflationary pressures in the economy (Benakovic and Posedel, 2010). They increase companies costs like transportation and production, while may reduce their profits and consequently stock returns. So, oil prices are expected to have a negative influence on the capital market. Faff and Brailsford (1999) for Australian equity return sensitivities show that these sensitivities to oil prices vary across industries. They found negative effects over the oil and gas, paper, packaging and transportation industries. Kilian and Park (2009) have studied the effect of oil demand and supply price shock on stock returns, finding that only oil demand shocks have a significant impact on stock returns. Fedorova and Pankratov (2010) used Brent oil price to examine the influence of macroeconomic factors on stock returns in Russia, revealing that this Brent oil price is the macroeconomic factor that most affects stock returns. Following Benakovic and Posedel (2010), macroeconomic variables cannot comprise all the information available in capital markets but stock prices react quickly to publicly information released. On the basis of this argument, the authors suggest the inclusion of financial market variables like the stock market index in the factor model. Previous authors like Chen, Roll and Ross (1986) have also used this variable and we will use each country individual stock market index (MI in the tables) representative as an independent variable. 9

12 Source: Own regression results. Notes: The estimation method used in this multifactor model has been the OLS with White heteroskedastic correction. Standard errors are in parentheses. ***= significant at 1% level; ** = significant at 5% level; * = significant at 10% level. α represents the constant term; MI the market index; IR the interest rate; π the inflation rate; CC the consumer confidence; IP industrial production and OP the oil return. The sectors here analyzed are Basic Materials (BM), Consumer Goods (CG), Consumer Services (CS), Financials and Banks (F; B), Healthcare (HC), Industrials (I), Oil and Gas (OG), Technology (Tec), Telecommunications (Tel) and Utilities (U). Although we have performed regressions over 250 individual company stocks, in order to save space we will only present the results for one randomly selected company for each sector and for each country, given that results were mostly similar among the five companies considered for each sector and thus can be generalized. All the other results will be provided upon request. Table 1 reports the results obtained for one of each of the 5 considered companies, randomly selected, for each sector for the United Kingdom stock market. Results can be generalized to the other companies. It is clearly evident that it is the stock market index which most affects company individual stock returns and in a positive manner. The interest rate variable, when statistically significant indicates a positive relationship with the company stock returns, which is also observed for the variable inflation in the UK market. Also, when statistically significant, the consumer confidence index indicates a positive relationship with individual stock returns, and for UK this happens for the financials, oils and gas, telecommunications and utilities companies sectors. As also stated by previous authors, oil returns have asymmetric effects depending on the sector under analysis but we have only attained a positive significant relationship in the UK for the technology sector. Faff and Brailsford (1999) found negative effects over the oil and gas, paper, packaging and transportation industries. We have attained negative relationships for the consumer goods, financials, banks, industrials, oil and gas and utilities sectors, which is consistent with the economic reasoning concerning this sectors nature. As such, we cannot clearly state for the UK market that oil returns and stock returns have a significant relationship, contrary to Kilian and Park (2009) and Fedorova and Pankratov (2010). For both the UK and US markets we have considered banks as an individual sector within the financial sector and for both we can only state that there is a positive influence of the market index over this stock returns. Moreover, whereas for the UK we see the interest rate exerting a positive influence, for the US market it is both the inflation rate and the oil price which seem to have a positive influence over these. Results for the US market are showed in table 2. Table 2: US individual companies stock returns by activity sector regression results: determinants of returns US Sector BM CG CS F B HC I OG Tec Tel U Company α MI IR π CC IP OP 9,187 1,167 1,266-4,330-2,577-0,220 0,035 *** *** -0,282-0,069-1,000-0,176-0,025 3,042 0,819-0,251 1,282-0,702 0,088 0,218 ** *** -0,023 0,968 1,180-0,129 * -1,803 0,823-0,073 ** -1,030 0,077 0,007-1,996 *** -0,034 1,116-4,688 0,419 0,139-2,164 0,278 0,713 * -2,435 0,114 ** 1,774 *** *** -0,263-18,580 1,127-0,023 1,372 0,583-2,644-1,445-3,885-0,293-0,071 11,253 *** 0,248 1,073-3,780-0,323-0,309 0,463 1,433 0,439-0,123 4,112-0,098 0,203 0,090 *** 0,099 0,349 * * -0,990 1,163 0,428 0,154 *** -0,014 1,398 *** 1,924 *** 0,646 *** 0,306 *** (3,527) (0,212) (0,212) (1,321) (7,014) (0,408) (0,143) (0,085) (0,532) (2,827) (0,164) (0,058) (1,422) (0,085) (0,181) (1,126) (5,983) (0,348) (0,122) (0,064) (0,400) (2,127) (0,124) (3,009) (0,181) (0,102) (3,375) (0,196) (0,044) (1,070) (0,178) (0,635) (5,900) (0,343) (0,069) (1,697) (0,064) (0,082) (2,704) (0,157) (2,967) (1,111) (17,758) (1,032) (0,121) (1,360) (0,102) (0,537) (0,509) (5,836) (0,339) (0,055) (8,930) (0,176) (3,343) (4,755) (0,276) (0,363) (2,935) (0,178) (0,144) (1,099) (2,187) (0,127) (0,119) (2,391) (0,066) (0,895) (1,100) (0,082) (0,412) (0,097) (0,045) (0,537) (0,176) (0,144) (0,066) Sample Size Rsqrt 0,191 0,320 0,097 0,094 0,146 0,245 0,527 0,052 0,364 0,103 0,110 11

13 Source: Own regression results. Notes: The estimation method used in this multifactor model has been the OLS with White heteroskedastic correction. Standard errors are in parentheses. ***= significant at 1% level; ** = significant at 5% level; * = significant at 10% level. α represents the constant term; MI the market index; IR the interest rate; π the inflation rate; CC the consumer confidence; IP industrial production and OP the oil return. The sectors here analyzed are Basic Materials (BM), Consumer Goods (CG), Consumer Services (CS), Financials and Banks (F; B), Healthcare (HC), Industrials (I), Oil and Gas (OG), Technology (Tec), Telecommunications (Tel) and Utilities (U). In the US market we can also observe that the market index is the one which affects all individual companies returns. Oil prices seem to negatively affect consumer goods, health care, industrials, oil and gas and the utilities sector, but these results are not statistically significant. Curiously, and similar to what have happened before in the UK market, oil price increases seem to decrease the oil and gas individual companies returns. We should expect the opposite sign effect, and given that these results are statistically significant we do not see the need for more detailed interpretations. Moreover, only the utilities sector in the US seems to be positively and statistically influenced by the consumer confidence index and the inflation rate results indicate a positive influence of this one over the financials and banks sector company returns. Table 3: Australia individual companies stock returns by activity sector regression results: determinants of returns Sector BM CG CS F HC I OG Tec Tel U Company α 1,388 5,340 3,356 2,435-1,861 5,422-0,205 10,088 3,349-8,209 (3,148) (5,823) (2,867) (10,665) (3,188) (3,510) (4,570) (13,522) (2,956) (9,092) MI 0,843 *** 1,045 *** 0,688 *** 0,997 ** 0,728 *** 1,036 *** 1,154 *** 3,170 *** 1,015 *** 1,489 *** (0,126) (0,234) (0,115) (0,428) (0,128) (0,141) (0,183) (0,543) (0,119) (0,365) IR -0,334-1,557-0,773 0,052 1,030 * -1,215-0,462-0,723-0,522 0,691 (0,126) (0,234) (0,115) (0,428) (0,128) (0,141) (0,183) (0,543) (0,119) (0,365) π 0,349 1,631 * 0,079-0,903-0,385 0,372 1,195 * -0,706-0,169 0,998 (0,485) (0,898) (0,442) (1,645) (0,492) (0,541) (0,705) (2,085) (0,456) (1,402) CC 1,217 7,416 9,785 ** 31,672 * -4,325 10,387 * 3,261-27,422 4,077 32,504 ** (5,369) (9,931) (4,889) (18,189) (5,438) (5,986) (7,793) (23,061) (5,041) (15,505) IP 0,010 0,111 0,074 0,254-0,038 0,074 0,076-0,305 0,001 0,488 *** (0,061) (0,112) (0,055) (0,205) (0,061) (0,068) (0,088) (0,261) (0,057) (0,175) OP -0,099-0,304-0,040-0,169 0,050-0,149 0,048 0,281-0,160-0,112 (0,064) (0,119) (0,059) (0,218) (0,065) (0,072) (0,093) (0,277) (0,060) (0,186) Sample Size Rsqrt 0,187 0,139 0,175 0,044 0,141 0,245 0,169 0,152 0,291 0,113 Source: Own regression results. Notes: The estimation method used in this multifactor model has been the OLS with White heteroskedastic correction. Standard errors are in parentheses. ***= significant at 1% level; ** = significant at 5% level; * = significant at 10% level. α represents the constant term; MI the market index; IR the interest rate; π the inflation rate; CC the consumer confidence; IP industrial production and OP the oil return. The sectors here analyzed are Basic Materials (BM), Consumer Goods (CG), Consumer Services (CS), Financials and Banks (F; B), Healthcare (HC), Industrials (I), Oil and Gas (OG), Technology (Tec), Telecommunications (Tel) and Utilities (U). 12 Results for Australia are presented in table 3 and these are worth of comment with respect to the consumer confidence index. Similar to Otoo (1999), Jansen and Nahuis (2003) and more recently Lin, Ho and Hsu (2009) we can also state that changes in the consumer sentiment are contemporaneously associated with market returns. So, positive (negative) changes in the sentiment tend to drive aggregate stock prices higher (lower) in the same period, at least for company sector returns like consumer services, financials, industrials and utilities. When we have a negative sign influence we have no statistical significance and so results may be ignores and generalized to state that the consumer confidence index is an important variable to be considered when analyzing the effects of macroeconomic variables over stock returns, at least for some markets. However, there have been reduced efforts in this sense in the empirical literature to establish this relationship among the two variables. Also, up to now we have only able to see a statistical significant relationship between industrial production and the utilities sector for the Australian market. The interest rate only influences

14 positively the health care individual companies sector, whereas inflation only revealed a positive influence over the oil and gas company considered, which is consistent with the fact that oil prices have a positive and straightforward relationship with the inflation rate (FRBSF, 2008). The results for the German market are presented in table 4, while those for Japan are in table 5. For the first time we cannot say that the market index is the variable which most influences individual companies, considered by sectors, sector returns in the German market. This positive influence is only verified in sectors like basic materials, consumer goods, consumer services and financials, which were somehow surprising results given the financial literature which emerged in this context. But curiously industrial production reveals to be a very positive statistically influencing variable in German companies in sectors like basic materials, consumer goods, industrials and technology, which in fact are very developed sectors in this market and which have highly grown in the period considered. In fact, Germany is one of the leading countries of the actual European economic and monetary unit. Moreover, as German usage of renewables has also been increasing, we may justify the fact that oil prices do not represent a significant influence over stock returns. But a much deeper analysis is needed in order for us to be able to justify all of the results attained here. Table 4: German individual companies stock returns by activity sector regression results: determinants of returns Sector BM CG CS F HC I OG Tec Tel U Company α 1,336-1,203 1,852 1,089-0,945 *** -0,930-4,551 *** 8,968 1,802 *** -1,128 *** (2,627) (1,855) (2,667) (2,845) (2,179) (6,510) (6,067) (6,278) (2,104) (1,431) MI 0,372 ** 0,222 * 0,832 *** 0,584 *** 0,687 0,183 1,753 0,235 0,413 0,264 (0,177) (0,125) (0,180) (0,192) (0,147) (0,439) (0,410) (0,424) (0,142) (0,097) IR -0,498-0,256 0,014-0,473-0,133 * -0,548 1,607-0,897 0,053 0,147 (0,177) (0,125) (0,180) (0,192) (0,147) (0,439) (0,410) (0,424) (0,142) (0,097) π -0,388 0,428-0,058-0,242 1,584-0,784-0,882-5,385-1,482 0,207 (1,036) (0,731) (1,051) (1,122) (0,859) (2,567) (2,392) (2,475) (0,830) (0,564) CC -0,220-0,165 0,077-0,280 0,093-0,283 0,112-0,691-0,142-0,049 (0,089) (0,063) (0,091) (0,097) (0,074) (0,222) (0,206) (0,214) (0,072) (0,049) IP 0,265 * 0,295 *** -0,212 0,107-0,079 0,723 * 0,200 0,754 ** 0,061 0,012 (0,158) (0,112) (0,161) (0,171) (0,131) (0,392) (0,365) (0,378) (0,127) (0,086) OP -0,041 0,002-0,100 0,003-0,061 0,029 0,165-0,241-0,056-0,016 (0,072) (0,051) (0,073) (0,078) (0,060) (0,179) (0,167) (0,173) (0,058) (0,039) Sample Size Rsqrt 0,065 0,063 0,110 0,104 0,111 0,019 0,094 0,093 0,103 0,047 Source: Own regression results. Notes: The estimation method used in this multifactor model has been the OLS with White heteroskedastic correction. Standard errors are in parentheses. ***= significant at 1% level; ** = significant at 5% level; * = significant at 10% level. α represents the constant term; MI the market index; IR the interest rate; π the inflation rate; CC the consumer confidence; IP industrial production and OP the oil return. The sectors here analyzed are Basic Materials (BM), Consumer Goods (CG), Consumer Services (CS), Financials and Banks (F; B), Healthcare (HC), Industrials (I), Oil and Gas (OG), Technology (Tec), Telecommunications (Tel) and Utilities (U). Finally, interest rate reveals to have a negative sign over the representative company considered for the health care sector, which is consistent with our prior assumptions about the influence of interest rates sign over stock returns. However, this negative influence is not generalized for all the countries under analysis as previously observed. In all the other countries, when for some specific sectors the sensitivity of the interest rate, or else its coefficient revealed to be statistically significant, we see a positive sign. These results are contrary to our initial expectation abot the two variables relationship but are in accordance with those obtained by Benakovic and Posedel (2010) for the Croatian market, also considering individual companies results. The Japanese market companies regression data obtained is even worse in terms of results interpretation. In this case the market index is the one which most influences sector individual companies returns and in a positive manner, similar to the other countries under analysis. Moreover, the consumer confidence index only statistically influences the basic materials company returns 13

15 randomly selected for the results presentation, and once again in a positive way, consistent with previous empirical findings. Table 5: Japanese individual companies stock returns by activity sector regression results: determinants of returns Sector BM CG CS F HC I OG Tec Tel U Company α -0,256-1,485-0,108-13,400-0,691 1,033-0,155 0,546-0,751-0,719 (1,840) (1,568) (1,278) (4,980) (0,989) (4,720) (2,318) (1,726) (1,932) (2,552) MI 0,980 *** 0,764 *** 0,489 *** 1,318 *** 0,564 *** 1,298 *** 1,066 *** 0,489 *** 0,743 *** 0,838 *** (0,176) (0,150) (0,123) (0,478) (0,095) (0,453) (0,222) (0,165) (0,185) (0,245) IR 1,964 2,920-0,331 17,138 * 0,478-0,150 0,778-0,564 0,409 1,377 (0,176) (0,150) (0,123) (0,478) (0,095) (0,453) (0,222) (0,165) (0,185) (0,245) π 2,192-0,924-0,451-8,268-0,643 1,701 0,964 0,761-2,056 1,128 (1,940) (1,653) (1,347) (5,251) (1,043) (4,976) (2,444) (1,820) (2,037) (2,691) CC 22,023 ** -3,465-1,748-21,635-3,932 6,875 5,997 10,602-1,950 6,608 (8,639) (7,363) (5,999) (23,381) (4,642) (22,159) (10,882) (8,102) (9,071) (11,983) IP -0,165 0,035-0,021 0,222-0,001-0,105 0,065-0,120-0,032-0,014 (0,067) (0,057) (0,047) (0,182) (0,036) (0,173) (0,085) (0,063) (0,071) (0,093) OP -0,199 0,171 ** 0,158 *** -0,063 0,100 ** 0,113-0,138-0,013 0,053 0,099 (0,081) (0,069) (0,057) (0,220) (0,044) (0,209) (0,103) (0,076) (0,085) (0,113) Sample Size Rsqrt 0,424 0,311 0,263 0,162 0,377 0,115 0,272 0,189 0,246 0,146 Source: Own regression results. Notes: The estimation method used in this multifactor model has been the OLS with White heteroskedastic correction. Standard errors are in parentheses. ***= significant at 1% level; ** = significant at 5% level; * = significant at 10% level. α represents the constant term; MI the market index; IR the interest rate; π the inflation rate; CC the consumer confidence; IP industrial production and OP the oil return. The sectors here analyzed are Basic Materials (BM), Consumer Goods (CG), Consumer Services (CS), Financials and Banks (F; B), Healthcare (HC), Industrials (I), Oil and Gas (OG), Technology (Tec), Telecommunications (Tel) and Utilities (U). 14 We may also observe that it is for the Japanese market that oil prices most influence, and in a positive fashion, sector company returns like those of consumer goods, consumer services and health care. In fact we should expect that besides the market index, oil prices should have more statistically significant relationships with each of the other companies sectors. In the case of Japan this positive influence in these specific sectors is mostly due to production and transportation costs influences, which will thus influence stock returns in the increasing sense. We can also observe from the tables that R 2 values are all very small for the generality of the countries, sectors and companies considered. Given the results that we have attained for individual companies we may conclude that other type of influence can be presented in these relationships. In fact, we can have disguised lagged effects or even clockwise effects, meaning that we should also explore the dynamic and nonlinear effects which might be happening among these variables. We could for example have oil prices influencing inflation and interest rates, and only then these will influence industrial production and stock returns, or some similar kind of relationship. But we leave this kind of analysis for other improving work. Moreover, it should also be interesting if we could separate the analysis between the pre and post-worldwide financial crisis to see if results change given that also previous authors point for separate effects. One example is that of Gregoriou et al. (2009) which found a negative relationship between interest rate changes and stock returns before the credit crisis period; however, the relationship reversed to a positive one during the credit crisis. In order to see if results change by using individual company data or general stock market sector indices returns we will next test the significance and explanatory power of all the macroeconomic variables previously described plus the world respective stock index sector return (as representative of the market index) over sector stock index returns, in total ten for each of the countries analyzed which sums 50 sector indices analyzed. Table 6 presents all country sector indices results obtained.

17 Materials (BM), Consumer Goods (CG), Consumer Services (CS), Financials and Banks (F; B), Healthcare (HC), Industrials (I), Oil and Gas (OG), Technology (Tec), Telecommunications (Tel) and Utilities (U). Regarding sector stock market indices, for all countries we see that the world respective sector return index is the one which most influences positively individual country sector returns. In fact, for the UK market only this variable seems to have a statistically significant impact over sector index returns. Unfortunately, we haven t got many different results as those already obtained for individual companies operating in each of these sectors. As presented, only the macroeconomic variables inflation, oil prices and industrial production seem to have effects over sector stock market indices, and for all the variables whose results revealed to be statistically significant seem to indicate that there exists a positive influence of these over the sector indices. For the Australian market inflation only exerts a positive influence over the industrials sector and for the US market it influences positively the health care, industrials and oil and gas sectors. As for industrial production results seem to indicate that there is only a positive impact of this variable over sector stock index returns for Germany and Japan. While in the last the impact is only significant over the oil and gas sector, for Germany this same effect is verified over basic materials, consumer goods and the financial sector. Finally, oil prices affect sectors positively like financials in US, Germany and Japan and that of consumer goods in Australia, Germany and Japan. It also affects positively basic materials in Germany, as well as consumer services, oil and gas and the utilities sector in Japan. We could now ask why we have presented these results in face of such insignificance of coefficients obtained. In fact, we asked ourselves this and have tried to see if these results are due to a bad choice of macroeconomic variables or if these depend upon periods or model choice. In order to answer this we have applied the moving windows estimation technique for periods of 60 month windows for each of the sector stock indices returns in each of the countries analyzed. Once again and due to space restriction we will only present beta estimates through for two sectors in the UK market: financials and consumer goods. Results are showed in figures 1 (for the financial sector) and 2 (for the consumer goods sector). These beta coefficients estimates are based on moving windows estimates where regressions for each index are done considering the same explained and explanatory variables for small samples of 60 months each. As such, we are able to obtain beta coefficients estimates for each macroeconomic variable considered through time given that each month the last 60 monthly observations are used to compute these same coefficients evolution through time. So, we can also observe if results statistical insignificance is always verified through time intervals samples of 60 months or even if the model specification used is the most correct one. 16

18 Figure 1: Moving windows estimates for 60 months time intervals regressions: financial sector stock market index in the UK market Source: Own produced results. Notes: These figures presents beta coefficients estimates obtained for the financial sector index stock returns in the UK market, by using small moving windows regression estimates based on the last 60 months observations. The x axis shows the betas evolution through time for the time period analyzed and also into account in each month estimate the previous 60 months values. The y axis represents the beta coefficients estimated values which goes from 0 (no sensibility of the stock index sector return to that macroeconomic variable) until 1.5 (high sensibility of the stock index sector return to that macroeconomic variables positive or negative). As observed in figure 1 and 2 the macroeconomic variables chosen do have effects over the specific sector stock market index returns, but these effects change through time and that s why the aggregate effect presented previously turned out to be so highly statistically insignificant in general. So, we do have a relationship between macroeconomic variables although it is not a stable relationship through time because it changes of sign throughout time. This means for example that until 2003 the interest rate has negatively influenced the financial sector returns but between 2003 and 2004 this effect turned out to be positive, or else that oil prices do not seem to have any statistical significant effect over the financial sector for the entire period for the UK market. So, changes of sign are not linear because for several periods we have high significance and for others there is simply no significance verified between the variables under analysis. Figure 2: Moving windows estimates for 60 months time intervals regressions: consumer goods sector stock market index in the UK market Source: Own produced results. Notes: These figures presents beta coefficients estimates obtained for the consumer goods sector index stock returns in the UK market, by using small moving windows regression estimates based on the last 60 months observations. The x axis shows the betas evolution through time for the time period analyzed and also into account in each month estimate the previous 60 months values. The y axis represents the beta coefficients estimated values which goes from 0 (no sensibility of the stock index sector return to that macroeconomic variable) until 2 (high sensibility of the stock index sector return to that macroeconomic variables positive or negative). Curiously in the consumer goods sector oil prices also seem to have no effect over sector stock returns, but conclusions to be taken from here need a deeper analysis. But for both sectors we have a positive and highly significance of the market index over sector returns as also previously concluded for the entire period. Turning to the consumers sector returns beta coefficients estimates we see that inflation rates had a negative effect over the sector index between 2004 and 2005 and again in the recent years of the financial world crisis. In that same period of 2004 we observe the interest rate positive effect over this sector, while consumer confidence and the production index impact have periods of unstable effects. This instability is not captured through a simple OLS model 17

19 and we need to use nonlinear models or even different time samples in order to be able to take deeper and better conclusions. This type of conclusions leaves room for a deeper understanding of these variables effect over companies and sector index stock returns. Given that both figures reveal the stability or instability of beta coefficients estimates throughout the sample period and this instability has been observed, future research should consider these different periods analysis and also the use of nonlinear models able to capture these unstable effects through time among variables. 5. Conclusions Our findings have beneficial implications for policy makers who are responsible for managing the economy but also for individual company s managers. In this work we have analyzed the effects of macroeconomic variables like inflation rate, interest rates, industrial production, consumer confidence, oil prices and the market index over both individual companies stock returns, considered by sector and for stock market index sectors for five developed countries (UK, US, Germany, Australia and Japan) using monthly data series for the period between March 1993 and February For the analysis we have used a multifactor model, having performed more than 300 individual regressions. Results indicate that the variable which most affects individual companies returns independently of their sector is the market index, while we are not able to establish a unique relationship between the variables under analysis for the rest of the considered macroeconomic variables. Moreover, we are not able to generalize these other variables results in terms of sectors and countries and this fact can be attributed to their different market characteristics. Despite this fact, we are able to state that our empirical results suggest the importance of the inclusion of the consumer confidence index to explain macroeconomic impacts over stock returns, which has upon to now been somehow discarded from the emerged empirical literature over the issue. Moreover, although even if we are not able to generalize this to all companies sectors and indices, we may conclude from our empirical analysis that companies returns sensitivities to macroeconomic variables change of sign depending over the sector and country under analysis. A curiosity is the fact that interest rate revealed to have a positive effect for most of the companies and countries under analysis, when we should expect from economic theory an opposite effect. As for general stock market country sectors indices also considered here we may conclude that inflation, industrial production, the market index and oil prices are the only variables able to explain some of the sector index returns, where all seem to have a positive effect. For all the dependent variables under analysis we may say that it is the market index the variable which most influence exerts over stock returns, although for Germany this conclusion cannot be taken for all companies and sectors under investigation. Finally, we have shown that these insignificant coefficients estimates obtained are not due to a bad choice of repressors, but yes to the instability of beta coefficients estimates throughout time which leaves room for future work. We may state that more research into these effects is still needed given that it seems that other variables other than those here considered may still influence stock returns. Moreover, it would be interesting to take a pre and post crisis, or for small sample periods observations along the entire time span considered, perspective analysis in order to see if results change as well as to generalize these effects for all companies of all sectors in all countries, for the ones considered here or even others around the world, which still need more economic reasoning and exploration. 18 References Adam, A. M., & Tweneboah, G. (2008). Macroeconomic Factors and Stock Market Movement: Evidence from Ghana. MRPA Working Paper Adjasi, K. C., and Biekpe, N. (2006). Stock Market Development and Economic Growth: The Case of Selected African Countries, African Development Review, 18 (1):

23 Singh, T., Mehta, S., & Varsha, M. S. (2011). Macroeconomic factors and stock returns: Evidence from Taiwan. Journal of Economics and International Finance, 2(4), Sohail, N. and Hussain, Z. (2009). Long-Run and Short-run Relationship between Macroeconomic Variables and Stock prices in Pakistan: The Case of Lahore Stock Exchange, Pakistan Economic and Social Review, 47(2), pp Tangjitprom, N. (2012). The Review of Macroeconomic Factors and Stock Returns, International Business Research, 5, 8, World Bank Report (2005), India. Role of Institutional Investors in the Corporate Governance of their Portfolio Companies. Yartey, C. A. (2008). Determinants of Stock Market Development in Emerging Economies: Is South Africa Different? IMF working Paper-WP/08/32 Washington, International Monetary Fund. Zhu, B. (2012). The Effects of Macroeconomic Factors on Stock Return of Energy Sector in Shanghai Stock Market, International Journal of Scientific and Research Publications, 2, 11,

24 INDICADORES ESTATÍSTICOS DO DESEMPENHO ECONÔMICO DA EMPRESA INDÚSTRIA DE MINERAÇÃO (90'S ): OLHANDO ANTES DA REVOLUÇÃO Mohsen Brahmi 1, S. Zouary 2 1 Faculty of Economics and Management, University of Sfax, Campus Zarruk Gafsa IAE, Tunisia. 2 Pr. Head Department of Economics, Higher Institute of Business Administration, University Sfax, Tunisia. Resumo. O objetivo deste trabalho é dedicado à apresentação de um dos principais nervos da economia tunisina, que é o depósito de fosfato de rocha no sudoeste da Tunísia integralmente detida pelo maior público mineiro Firm CPGT Tunísia, que na verdade é apenas o pólo industrial na Mineração bacia região da Tunísia. Na verdade, a Tunísia é mais de um século no setor de mineração, esta experiência tem sido colocado na linha de frente dos principais países produtores de fosfatos comerciantes em todo o mundo. Embora, Tunísia qualificou o segundo país no mundo tem delegado a recuperação de fertilizantes fosfatados com uma experiência que ultrapassa meio século neste negócio. No entanto, apesar da intensa competição, a indústria de fosfato da Tunísia nos últimos anos (90's-2010), principalmente no final de 2008 após a visão sem precedentes dos preços das commodities no mercado mundial de fosfato de fertilizantes fosfatados WPFP, ganhos de eficiência notáveis melhorar o setor de base financeira e que tenham direta consequências sobre o desempenho econômico. Palavras-chave: Empresa pública mineira, A perícia, Os ganhos de eficiência, Tunísia fosfato,. STATISTICAL INDICATORS OF ECONOMIC PERFORMANCE OF THE MINING INDUSTRY FIRM (90'S ): LOOK BEFORE THE REVOLUTION Abstract. The purpose of this paper is devoted to presenting one of the main nerves of the Tunisian economy, which is the deposit of phosphate rock in southwestern Tunisia wholly owned by the largest public mining Firm CPGT Tunisia, which is actually the only industrial pole in the Mining Basin region of Tunisia. In fact, Tunisia is more than a century in the mining sector, this expertise has been put in the forefront of major producing countries phosphates merchants worldwide. Although, Tunisia has qualified the second country in the world has deputed the recovery of phosphate fertilizers with an experience that exceeds a half-century in this business. However, despite intense competition, the Tunisian phosphate industry in recent years (90 s-2010), mostly late 2008 following the unprecedented overview of commodity prices in world market for phosphate fertilizer phosphate WPFP, efficiency gains remarkable improving the financial base sector and which have direct consequences on economic performance. Keywords: Expertise, Efficiency gains, Public Mining Firm, Tunisia phosphate. INTRODUCTION The natural deposits of ground Phosphate Rocks located in north of Africa are closely linked chains of mountains of the producing countries phosphates dealers (mainly Tunisia, Morocco and Algeria). These channels are spread over a large area where the starting point is the chain of mountains southwest of Gafsa in Tunisia, passing by Algeria to settle finally in front of the Atlantic Ocean in Morocco. Considered the first and oldest in the exploitation of these mineral deposits in North African, Tunisian industry phosphate (the discovery of Tunisian phosphate was in 1897) was able to acquire for that purpose, hot knowledge in this area mining activity that exceeds more than a century of 23

25 exploitation and international marketing of this product gray 2 sub-field which is fully localized in the Mining Basin Gafsa in Tunisia (MBGT). Thus, with the Tunisian independence in 1956 until today, the Tunisian phosphate industry (TPI) which is represented by the large public Firm TCGP (Tunisian Company Gafsa Phosphates) has continued to proliferate more to finance a large share of other economic activities in the country. Although, like other sectors of economic activity, the global Tunisian phosphate industry has made some economic shocks in times of crises such as oil seventy years and debt in the mid-eighties. Yet, the Tunisian mining industry has resisted facing this very difficult international situation for any mining company exporting whatever their economic size. As soon as the late eighties - under the efforts of Government assistance, through various policies to encourage research into new fields, exploration of markets and partnership - the Tunisian phosphate sector (TPS) has regained its vitality through the technological modernization of working tools and could therefore generate a significantly positive outcome and that is really real date overview of this industry for a more phosphate promoter since the mid-ninety today. In this context, the Tunisia Phosphate Gafsa Company (TPGC) was engaged in a program of reorganization 3 and technological upgrading to significantly enhance its profitability, to handle to allow him to compete with foreign (Morocco the main competitor in the North Africa) more intense global and precisely address this global economic environment nowadays, unfavorable to the exporters, despite the plan of support and backup in various forms of aid by the Government. How has been manifested Tunisian phosphate industry to cope with this latest world economic crisis of 2008 and just after tow years before the revolution 2011? Strongly observed (Tunisian Central Bank report, ) that Tunisian phosphates Sector TPS, presented by the Leader Company TPGC and subsidiary Group Chemical Tunisian where recovery is phosphate fertilizer, has continued its vigorous development in the global economic crisis of Although, the price of phosphate has more triple from $ 45 per ton in early 2007 to more than $ 324 per ton in 2008 (USGS , World Bank 2009). This has generated highly remarkable efficiency gains leading to strengthening the financial base of TPS and improved accordingly Tunisian national GDP 4. Despite the significant level of current sulfur imported ($ 810 per ton in late 2008), the latest global economic report (Davos Economic Report 2009 to 2010, Fitch Rating Agency 2010) showed that the wisdom of the Tunisian economic governance, and case here Tunisian phosphate industry, brought to fruition in the event of crisis and cyclical changes to cope with any economic damage and circumvent these adverse effects for advocates. We chose to structure this research paper into three sections, namely: In the first section of this article, we outline the general historical overview of this large public Firm Tunisian TCGP more than a century in its mining field, is taken as the framework for implementing our research study in this paper. The second section of this paper is devoted to showing the rank occupied by this large global firm public before Tunisian TPGC world's leading producers of phosphates (such as USA, Morocco, China..) with their economic weight in the global market phosphate fertilizers (GMPF) through their production potential of this ore. In presenting its contribution to the Tunisian economy in terms of 24 2 Returning to history since antiquity, the region of Gafsa Mining Basin (G.MB) in Tunisia was totally invaded by the sea, but we have attended several geological changes in northern Africa, in the case of Tunisia, where the level of the sea in the said period began to sink deeper and eventually the whole region (G.MB) has become Tunisian land without water. It remains in this region, according to geologists, the underwater environment (the various types of fish skeletons, seaweed, algae, various marine plants... etc..): Organic substances in water rooted mountains led by following the formation of various minerals, mainly phosphate (P). As is well known, world agriculture (FAO, 2010) is remarkable in need of phosphate fertilizers that are in high demand in the world market for phosphate fertilizer (WMPF). 3 The real objective was compression costs of production, while encouraging more in search of new mineral deposits by the mining area, in part cons acceleration for the closure of underground mines where the cost operation was very high. 4 GDP indicator: Gross Domestic Product.

26 various mineral products exported on a significant period of time with a deduction of the results achieved and future perspectives of these ores and those imported. The final section will focus on development and economic performance of the Tunisian Industry phosphate. We focus on the reputation and image of this Tunisian Company of phosphates in the eyes of global Rating organizations, especially the annual contribution of the agencies Fitch Ratings , dealing with change current international cyclical. "... The opinion of the International Monetary Fund over economic policy in Tunisia is very positive and is doing well despite the global financial crisis and the severe fluctuations in oil, metals, mining and food... We don t have no fears for next year 2009, although in global, it will not be easy. In Tunisia, it works properly...''(mr. D. Strauss-Kahn, Managing Director of the International Monetary Fund, November 2008). 1. GENERALIZED OVERVIEW: ACTIVITY OF THIS GREAT TUNISIAN PUBLIC FIRM, TPGC: TUNISIA PHOSPHATE GAFSA COMPANY Tunisia is a pioneer in the world regarding the field of extraction of phosphates and industry phosphate fertilizer. This is connected directly to the prestigious and oldest Company in Tunisia, namely, TPGC: Tunisia Phosphate Gafsa Company (in French called CPGT). It occupies the first places 5 in the world in terms of the production of phosphate (5th place) with an annual production in late 2009, which exceeds 8.5 million tones of phosphate raw (INS, Tunis, 2009) exported to fifty countries worldwide. Thus, in order to witness the historic quality of the Tunisian phosphate and Ports in Basin Mining of Gafsa Armiger 6 and M. Fried. (1957), have compared 12 sources of phosphate rocks in terms of quality organic... they could finally work out of such research as the Tunisian phosphate in region south-west of Gafsa is the best among the other samples of phosphates rock, it becomes after him the phosphate rock of South Carolina in the United States' Brief History of TPGC: Tunisia Phosphates Gafsa Company In 1885, in the Gorge "THELJA" near Métlaoui in Gafsa, a French paleontologist, Philippe Thomas(1885), prospecting rocks was found a layer of lime phosphate. Born then, the Tunisian Phosphates Gafsa Company TPGC. (See graphics card with the delivery of Gafsa phosphate to the ports of embarkation for export below) Tunisia is the 2nd country in the world in the exploitation of phosphate fertilizer after Morocco. In addition, Tunisia occupies the 3rd place in terms of export of the product phosphate (2009) and 5th place for the production of that "underground-gray (According to latest statistics from the Association of Industries fertilizer, USGS ). 6 Armiger.H and M. Fried., 1957, 'the plant availability of Various Sources of phosphate rock. Soil Science Society of America proceedings',pp (1957).

27 Figure 1: Tunisian Phosphate in Mining Basin of Gafsa 26 Table 1: Chronology of the time of foundation of Company Phosphate Gafsa Tunisia since the discovery of mineral deposit in Date of discovery of mineral deposits in Tunisian south-western near the mountains of Métlaoui Gafsa by the French geologist Philip Thomas Appointment of the Gafsa Phosphate Company and Railway Gafsa (CPGCFG) as the first operator of mining offices Métlaoui. Followed by the first line linking ferrate Métlaoui to seaport of Sfax Date of opening of the first under-ground mine Métlaoui (Gafsa) 1903 Date of opening of the second under-ground mine at Redeyef (Gafsa) 1904 Date of opening of the third under-ground mine at Moulares (Gafsa) 1905 Creation of the Stethos (Tunisian Company operating phosphate) (Gafsa) 1920 Opening of M'Dhilla mine operated by the Company of Phosphate Tunisia Turning SIAPE (Subsidiary industrial phosphoric acid and fertilizers) 1956 Progressive Nationalization of different Companies under the direction of the one leader Tunisian Company fully capitalization of Tunisian Phosphates Company Tunisian M'Dhilla

28 1966 End alignment Railway-Gafsa Sfax, so that it becomes independent from that date Merger between the CPGCFG Company and phosphates M'Dhilla 1970 Date opening of the mine Shib-Mrarta and starting in 1975 the career of mining Moulares 1975 Start the quarries of mining Moulares 1976 The merger between Stethos CPGCFG and under the sole appointment of the TCGP wholly owned by the Tunisian Government Switching quarries mining Kef-chfaire 1980 Start mining quarries of Umm-lekhcheb 1986 Start mining quarries of kef-dur 1989 Switching quarries mining Redeyef 1991 Start of mining quarries DJelebba 1994 Appointment of a single director general for TCGP and subsidiary chemical group 1996 The merger into a single business management Signing of Partnership with Indian Companies, where TCGP and chemical group that holds more than 70% of capital Date to Enter into force of this Tunisian-Indian partnership (the Company TIFERT Indian) for the recovery of phosphate fertilizers, largely intended for export. Source: Documentation and planning Data, Founded in 1897, Tunisia Company of Phosphates Gafsa, TCGP, is a company more than a century, which is the public operator of phosphates in Tunisia. Its expansion is unprecedented in the underground opening that extends to other areas of the mining area of Gafsa. Indeed, extraction of phosphate product began in Tunisian Mining Basin more than 110 years ago. Although, in global level, Tunisia is the second country after Morocco which began to recover in the last fifty years in the Tunisian Chemical Group in Sfax, which is a subsidiary company directly linked to the parent TCGP, a remarkable share nearly 80% of this natural product in phosphate fertilizers quality (phosphoric acid, DAP, TSP.. etc..) very competitive and great demand in World Market phosphate fertilizers WMPF. Figure 2: Graphics of the mining area of Gafsa, where Tunisian TCGP Company operates phosphate mining Tunisia exports a large share of phosphate fertilizer into European Union EU saw bilateral trade well as geographical proximity Mediterranean, while the other is destined for other countries from four continents. (See illustrative diagram in the box below) 27

29 Figure 3: Export of Tunisian phosphate fertilizer to some fifty countries around the world This Company is owned 90% by Tunisian Government, the Tunisian Company Gafsa Phosphates T.C.G.P, was founded in effect: To exploit phosphate reserves in the Mining Basin of Gafsa in particular. Undertake enrichment of ore to obtain a marketable quality. To commercialize the phosphate product in the most favorable economic conditions possible. To conduct surveys and geological research. Thus, the TCGP Company of Tunisian phosphate had long been and so far as the single most major industrial center located, over one hundred years in the Mining Basin into southwest Gafsa in Tunisia where the lives of over 380,000 family homes Gafsa mining basin. This largest citizen Company currently employs in 2010, more than 5,200 employees including 512 managers (top and middle) and ranks fifth in world production, although the third place among countries exporting large phosphate product. Human Resources 1% 6% 4% 32% 57% senior managers middle employees workers Contractual 28 Figure 4: Graphics card on the allocation of human resources by the Tunisia Company Gafsa Phosphate However, this position of this large Tunisian Company mining (T.C.G.P) worldwide, is caused by its potential reserves estimated at Million tones with a very rare quality, its very popular with customers in the world market for fertilizers and phosphate, its yearly production is 8.5 million tones

30 Million tones ATAS/PROCCEDINGS 17º WORKSHOP APDR ISBN of phosphate merchant in 2010 and projected 9.5 million tones from that product for years according to forecasts7 of medium and long term research into development center of T.C.G.P in Metlaoui Tunisia. (See box below on the production of Tunisian phosphate Merchant from 1900 to 2010) Figure 5: Evolution of yearly Tunisian natural phosphate between ( ) Source: Direction T.C.G.P, Management center of phosphate production, Note: graphic representation by simple weighted average for each period of 11 years in simple smoothing until 2010, it forecast at predetermined by the previous two years Based on the shape of the graph on the production of natural phosphate on a time interval that exceeds more than a hundred years (1900 to 2010), most strikingly, two periods of sustained growth: First phase ( ) who in 1938 reaches peak near 3.2 million tones of phosphate produced. Another time the most remarkable stretches over a longer period, from 1946 to graphically present with growth typically continues until 2010, which is mainly due to good cost control by the company TCGP and by using modern ICT technology tools most sophisticated mining ores and recovery the Tunisian phosphate product. All these assets held by the Tunisian Mining public enterprise, contribute much to growth unprecedented production volume of Tunisian phosphate fertilizers until today. Although, a difficult time passing ( ) between the two periods when the production rate was tilted to the lowest level, the most shocking noted throughout the economic life of this great Tunisian public Mining Firm (TCGP) up until today. While, the causes are well known in fact that are related to the crisis 8 in 1929 and very serious disruption affecting all sectors of global economy. 1.2 Mining plant Currently, the TCGP Company holds four mining centers consist of two underground mines and seven surface mines provide over seven laundries Tunisian phosphate enrichment. Mining activities in the 7 The Centre for Research and Exploitation in Métlaoui Tunisia (CRVM) is one of the main nerves of the Great Tunisian Mining Company TCGP. Its main task is to predict the annual production rate, while applying predictive models of means and long term (generally over periods of 10 years and over). The center promotes the research and also to exploit other areas of geological mining Gafsa Tunisia to maintain the one hand, and increase the volume of production of phosphate, on the other hand, through the new Technology ICT resources used and operational considered the most modern used by the world's leading producers such as Morocco (1st world producer). 8 Crisis 1929 (Black Thursday on October 24, 1929) is the worldwide economic depression which was evidenced by the collapse of several global macroeconomic indicators such as: the worst economic recession in the history of the world economy, an unprecedented deflation followed by a significantly higher rate of unemployment, falling stock prices and the bursting of the speculative bubble leading a stock market crash. 29

31 TCGP Company are located in five main sectors in mining region of Gafsa Tunisia which are distributed as follows: For the region of Mining Métlaoui, there were two areas which are: The sector "Kef-Métlaoui Schfaier" includes quarries Kef Shfaier and laundries of Métlaoui. The sector "Kef-Métlaoui Eddour" includes quarries and Laundromat of kef Eddour. For other regions of the mining area: The sector "Redeyef" includes mine Erg-Lasfar, quarries and laundry Redeyef The sector "Moularès" includes mine Mrate, quarries and laundry Moulares. The sector "M'Dhilla" includes quarries and Jellabia Mzinda-andTHE M'Dhilla of laundries. 2. GLOBAL POSITIONING AND CONTRIBUTION TO THE NATIONAL ECONOMY In regard to his rank in the world who can be classified as a fifth Tunisia powerful producer (USGS 2009) to the world's leading producers 9 of phosphate products and its ability to employability, as large industrial center in the mining region of Tunisia. This prestigious Tunisian citizen mining Company has a remarkable contribution to the gross value added of the Tunisian economy and the trade balance. Its turnover rose from 403Millions dinars in 2007 to 1,406 million dinars in 2008 and 1854 the following year. (Annual Report of the Finance Department of the TCGP, 2009) 2.1 Extraction of high quality of phosphate requested The extraction of phosphate raw -gross- reached in 2009 (INS Tunisia, late 2009), 12 million tones is currently extracted from 10 units and is transported to processing units into Sfax for enrichment, which resulted in the production of nearly 9 million tones of phosphates commodities (merchants). TCGP Company has placed Tunisia with this production of phosphate as the 5th largest global wholesale producer countries of phosphate immediately after Morocco, the United-States of America, China and the Russia. And the third largest exporter of the product. 30 Table 2: Ranking of the eight major producers of phosphate rock by volume and world share in 2006: Natural phosphate of Tunisia on Fifth Rank. Major Producing Production volume World share in percentage (%) Natural phosphate (In million tones) Countries United States (1) China (2) Morocco (3) Russia (4) Tunisia (5)* Jordan (6) Brazil (7) Syria (8) Source: USGS, May Notes: (1) to (8) is the descending order in terms of production volume of natural phosphates by the major global producer; * Data are for Tunisia in 2009 (BCT.2009) who ranked fifth place in the world. Other data are attributed to the USGS in 2006, given the lack of data by country of origin. 9 The world's leading producers of phosphate product are classified as follows: MOROCCO (1st), the United States, China and the Russia. So, Tunisia in 5th place worldwide.

32 The TCGP Company operates sedimentary deposits of phosphates multilayer separated by spacers marl and limestone. The operation of these pools which was entirely in underground mines has been gradually transformed to a current open pit from which currently reaches more than 87% of the total extraction Exportation in World Market Nearly 15% of phosphate rock extracted by the TCGP Mining Company is exported to 15 countries located in Asia (the largest share to India as the main customer in this region), mainly in LATIN AMERICA and EUROPE. The remaining 85% is processed locally in Sfax Chemical Group factories (GCT) to phosphoric acid and rich variety of fertilizers most of which is exported to these countries by port of Sfax. Table 3: World production of phosphate commodities between (Millions tones of ore): Tunisia to 5th place worldwide United-States China Morroco Russia Tunisia Other World Phosphate 41,2 26,4 25,7 11,2 8,1 35,2 143,8 P 2 O 5 * 14,6 8,2 8,3 4,8 3,7 11,6 47,3 Source: Association of Fertilizer Industry, USGS Notes: The production of phosphates indicated by the Fertilizer Industry Association are slightly superior to those statistics from USGS, but the proportion of phosphoric anhydride (P2O5) in phosphate remains the same (31.2%).* The rate of phosphorus pentoxide (P2O5) in the phosphate is 31.2% on average. Tunisia into 5th rank in world United-- States China Morroco Tunisia World Figure 7: World production of phosphate (Million tones of ore) Phosphate P2O5 0 According to statistics from the Association of Fertilizer Industry, USGS 2008, Tunisia ranked fifth worldwide by two indicators of production (yearly production volume of crude raw phosphate and the level of the ratio of phosphorus pent oxide P2O5 phosphate in Tunisia). However, 2008 has shown a greater demand for Tunisian phosphate rock with an export volume has more than doubled in 2008 to about Million Tunisian Dinars, against only 71 in Million Tunisian Dinars (See Table 4, below). 31

33 Table 4: Main exports (Mining, Phosphate and derivatives) Years Phosphate rock raw Phosphoric Acid D.A.P Triple Superphosphate Salt Zinc Source : National Institute of Statistics (INS), Tunis Note: Unit : Million of dinars Figure 8: Evolution of the main exports: Mining Phosphate and derivative ( ) 32 An overall view on the evolution of various phosphate ores exported from Tunisian Mining Bassin Gafsa (Mining area) displays an increasing rate of world exports of raw phosphate and its various components in mineral fertilizers as shown in the chart above. Strongly held in 2008, flying over the price of phosphate fertilizer in the World Market PFWM, the trade balance is highly comfortable with these surplus yields gains generated by these products and derivatives exported phosphate (phosphoric acid Million Dinars, Million Dinars raw phosphate, DAP Million Dinars and Triple superphosphate 767 Million Dinars). But this export volume is almost halved in 2009 for these various phosphate fertilizers with a return to the state of price stability in the market PFWM after the unprecedented rise in prices in the end Moreover, since 1994, the Tunisian government has decided the unification 10 of the general direction of the TCGP and Chemical group by appointing a single president. It was followed in 1996 by the 10 In 1994, under unification, the phosphate raw reached 32.9 Million Dinars TUN. (Echoes Tunisian Phosphate. 1994).

34 merger of the two sales divisions (exported raw phosphate posted in 1996, worth 35.7Million Dinars TUN), hence the rate of export of this product moderately accentuated to around 40 Million Dinars TUN, reaching in 2008 the highest price Million Dinars TUN. In 2009, prices for exported raw phosphate product were recovered near 59.9 Million Dinars TUN, after 2008 qualified more comfortable, its profits from the export of various phosphate fertilizer mining Tunisia, for this great public enterprise Tunisian mining TCGP. All these difficult global economic conditions, in contrast, conferred on the Tunisian industry phosphate more cohesion, efficiency and profitability made by exporting the phosphate product and its derivatives in the world market for phosphate fertilizer PFWM. (See the following table on the main products imported mineral enrichment between ) Table 5: Major Imports (Mining, phosphate and derivatives) Years Sulfur Ammonia Source: National Institute of Statistics (INS), Tunis Notes: Unit: Million of Dinars (UNITY: DINARS) MILLION Figure 9: Main products imported ( ) 33 By analyzing the values of sulfur and ammonia (see Table 4 and Figure 7 above) imported to enrich the Tunisian phosphate fertilizer for export; we note that average prices of both imported goods for

35 the period ( ) are around, respectively, 75 Million Dinars for the sulfur, and 52 Million Dinars for ammonia. But from 2003 until now there has been a rise in prices on the world fertilizer market; the respective mean values of ammonia and sulfur are in the order of 289 Million Dinars and 125 Million Dinars. However, the highest peak of these two products is displayed in 2008 ( Million dinars for sulfur and Million dinars for ammonia) of course year of world financial and economic crisis. Moreover, most strikingly in 2009 from the graph that the prices of those products are down almost the same damping averages from previous years 2006 and Development and economic performance of the Tunisian Industry phosphate: Role assigned to the Leader Mining Company. The phosphate industry in Tunisia will experience a great development in the coming decades with very large potential reserves of rare quality and the future investments already made, also to the restructuring program upgrade already followed. (Report of the BCT, ) For its development, TCGP Tunisian Mining Company has planned investments of around 128 million Tunisian Dinars, under the 10th structurally plan, which has already helped to achieve the horizon of the plan with production of 8.5 million tones of Tunisian phosphate rocks. The TCGP will have in future prospects, at least maintain production at current levels, improve productivity and compress costs of production and consumption of energy. 3.1 The extraction of phosphate quality: Facts and Estimates The extraction of phosphate rock is estimated to reach finally 2010, 12 million tones (phosphate Tunisian TCGP, 2010), which will produce 9 million tones of phosphates rocks. In addition, chemical, radiological, and mineralogical conducted by several laboratories around the world 11 indicate that the Tunisian phosphate Gafsa, produced by the largest public Company Tunisian citizen TCGP, is one of the most high quality rare phosphates in the world strongly requested. This places Tunisia in the world's third largest export phosphate mining product compared to leader exporter of phosphates (USA, Morocco etc.) Development efforts and contribution of TCGP, economic performance against the last world economic crisis: International Notes issued by Fitch Ratings Agency to phosphate Tunisia The Tunisian phosphate industry is one of the main nerves to the Tunisian economy. This is illustrated by the notes 12 of the Agency Fitch Ratings (AFR) published 13 recently (late ) for this great Tunisian Mining Firm showing its good image in the world and its future trends that will keep pace with growing strong, albeit this situation unfavorable world economic crisis. Currently and through this report 14 AFR (2009), the Tunisian phosphate industry will undergo a great development in the coming decades, thanks to its significant potential reserves that are very rare worldwide, and also for investment, reported by 11th ( ) government Plan, already made and also to ongoing programs of technological modernization, restructuring and upgrading already followed since 1996 by the great Tunisian Mining Company, TCGP. For increased its level of productivity and increasing the pace of development, this large Tunisian Mining Company has planned a investment in the order of 158 million Dinars in the 11th Plan ( Research group phosphate rock, Fertilizer Industry Association, USGS The confirmation notes to the TCGP Tunisian Mining Company by Agency Fitch Ratings (2009) are: * For the Long Term (LT): AA (TUN) * For the Short Term (CT): F1 + (TUN) stable character. 13 Agency Fitch Ratings: is an international rating industry agency, which assigns notes to Great International Firms yearly, based on each industry report, production technology and the export volume reduced. 14 The agency report was published recently in August 10, 2009.

36 2011) allowing it to reach the horizon of this plan an estimated production of 9.5 million tones of Tunisian phosphate. Moreover, facing this economic crisis that has severely affected most countries of the world together with their companies irrespective of their economic size, the great Tunisian Mining Company TCGP expected in future years more difficult economically, maintain production at least at the current level, improve their technological capabilities (ICT) in productivity, operating and recovery of minerals while reducing costs at various stages of business and marketing. In this framework, Fitch Ratings has confirmed, in fact, its ratings in 2009 at the international level for Tunisian Mining Company, TCGP. The new ratings reflect the strong performance of the Tunisian phosphate industry, despite these difficult 15 economic changes worldwide, which further contributes a remarkable development of the financial base of the Tunisian economy. However, these ratings assigned by Fitch Ratings to the Tunisian large public Mining Company reflect the excellence of the financial health of this great Mining Company that is directly related to the high demand for phosphate fertilizer quality worldwide for agriculture fertilization. This explains the surplus in trade balance in late 2008, when the price per tone of phosphate product has triple, exceeding 400 Dollars per tone, representing a huge win for the Mining Company and, Tunisian economy too (the Tunisian GDP has been growth remarkably). From an economic standpoint, the Tunisian phosphate industry has continued in recent years ( ), to take advantage of favorable global environment characterized by continued demand ever higher and the pursuit of increase in sales prices of phosphate and fertilizers. Table 6: Financial situation of the CPGT between early Incomes of the company (in Million Dinars) ,4 Operating Margin EBITDA *in% ,5 78,1 Level of debt (in million Dinars) ,3 54,8 Treasury Business (in Million Dinars) 178,9 215,4 961 Source: Annual reports of Fitch Ratings (2006, 2007, 2008, early 2009) Note: * EBITDA can be calculated from the account of the result in two ways: - Additive Method (EBITDA = Net income + interest expenses + tax + depreciation expense) - Subtractive method: (EBITDA = turnover-shopping-other external costs-personnel costs). This Leader Tunisia Mining Company TCGP has made in the end 2008 earnings MTD cons 414 MTD in 2007, a profit of 243%. Boasting the world price of phosphate is strongly increased after several years of stability (prices triple from medium 60 dollars / tone in the 2007 to over 400 dollars / tone in the 2008 and up to half of the year following 2009). Thus, the operating EBITDA margin increased from 37.5% in 2007 to 78.1 in late 2008, a differential gain of 40.6%. It is obvious to remark that this Tunisian Mining Company exhaled, finally in 2008 and early 2009, a strong cash flow exceeding 961 Million Tunisian Dinars, four times over the year before. Although, debt levels are unpacking a rate of 36.5% to end 2008, into lower debt to the order of 54.8%. However, it is important to remember that this great public Tunisia mining Firm has signed a jointventure under the partnership in the phosphate with Indian Companies for the installation of a phosphoric acid plant in Skhira Sfax and another in Gafsa Mdhila. The TCGP Mining Company and its subsidiary Chemical group hold more than 70% share in the joint-venture Tunisian-Indian. It is Faced to this financial crisis and global economic conditions, the Tunisian Government played a key role in aid and support for Enterprises (SMEs, etc.) most affected in order to keep out at less economic damage.

37 estimated that the production capacity of these plants located in the region of Sfax -in Tunisia- is the order of 360,000 tones per year with an increase in production rate for previous years. To this end, TCGP Tunisian Mining Company consider from the end of 2010, under the partnership agreement the Tunisian-Indian, to increase its own production of the phosphate mining in region of Gafsa Tunisia to reach 9 million tones per year, in order to cover actually basic needs of new plants already installed in areas of Sfax and Gafsa to the recovery of phosphoric acid in high demand in world market of phosphate fertilizer WMPF. CONCLUSION Tunisian phosphate industry has continued to thrive since its discovery in 1897 in the mining region of south-west of Tunisia. After independence, Tunisia has merged all mine operators to increase their competitiveness and their ability to produce in a great single Company named Tunisian Company of Gafsa Phosphates TCGP and its subsidiary Chemical group for recovery and enrichment phosphate fertilizers that are in high demand for agriculture by importing countries (mainly European Union and India) in the world market for phosphate fertilizers. Nowadays, the Tunisian phosphate industry has continued, despite the recent global economic crisis of 2008, take advantage of this international situation following the increase of high demand for phosphate products to enhance agriculture, adding to this fact, the unprecedented price soared phosphate rock in late 2008 that has more contiguous triplet (3 x) near the 400 dollars per tone of phosphate. To this end, the Tunisian phosphate industry, which represents a major economic base activity for the country, has been able to generate surplus cash bypassed for the development of the sector itself a part. The second is intended as a value added to national GDP and increase the capital base of the country as a direct result. Currently, in late 2009 and early 2010, prices have almost stabilized with a sort of equilibrium between supply and demand for the fertilizers phosphate product into world market fertilizer phosphate WMFP. Thus, the Tunisian phosphate industry continues to grow with a steady pace with their production opportunities at the future while pressing: Diversification of phosphate fertilizers Improving the overall quality Reducing production costs and any kind of waste Use of ICTs as tools for technological modernization to confront foreign competition (before entering the new entrants: in 2020 Algeria and Saudi Arabia in 2016) and armed with technological potential from the phase of operations until marketing of mining product. These are articulators points of the strategy implemented in recent years, despite the remarkable effort of all officers and managers are an essential support to achieve this targeted objective by senior management of this Tunisian Mining Company. 36 BIBLIOGRAPHY CPGT, (2012), Evolution of CPGT phosphate production in million tonnes, LAB recherché CPGT. Hubermont, (1930), Treize homme dans la mine. Paris: E-Publishing. Armiger, and Fried, (1957), The plant availability of Various Sources of phosphate rock. Sustainable Sources. CPGT, (2010), Echoes Tunisian Gafsa phosphate. LAB recherché CPGT INS, (2012), Annual Report of exports of phosphate fertilizers. Institut Nationale des Statistiques,

39 PROFILING HIGH-GROWTH ENTERPRISES IN PORTUGAL Elsa de Morais Sarmento 1, Nikos Theodorakopoulos 2, Catarina Figueira 3, Alcina Nunes 4 1 Departamento de Economia, Gestão e Engenharia Industrial, Universidade de Aveiro, Portugal. 2 Aston Business School, UK. 3 Cranfield University, UK. 4 GEMF Grupo de Estudos Monetários e Financeiros, Faculdade de Economia da Universidade de Coimbra, Portugal, Escola Superior de Tecnologia e Gestão do Instituto Politécnico de Bragança, Portugal. Abstract. This paper describes employer enterprise dynamics in Portugal for high-growth and gazelle enterprises for the period , using the methodology by Eurostat/OECD. It discusses stylized facts related to performance and employment by size, region and sector, to a detail which has not been previously considered, uncovering potential business areas of growth which are of prime importance for the country s economic growth and development. Two parallel perspectives are provided, by turnover and by employment. We also provide a comparison between Portuguese firms and some of their European counterparts. This contrast highlights differences in performance related to underlying national framework conditions, and specifically to the regulatory and cultural environment in which Portuguese firms operate, which seems to be instrumental to the understanding of their poorer performance. Keywords: Firm Demography, Framework Conditions, Gazelles, High-growth firms, Portugal Resumo. Este artigo descreve a dinâmica de empresas portuguesas de elevado crescimento e de gazelas, para o período , utilizando a metodologia do Eurostat/OCDE. Foram também adoptadas duas perspectivas diferentes para o apuramento destas empresas, a perspectiva do volume de negócios e por outro lado, do emprego. Ao longo do estudo, abordam-se alguns fatos estilizados relacionados com o desempenho e volume de emprego destas empresas, desagregados com um nível detalhe considerável, por região, dimensão e sector de actividade, relevando alguns sectores de negócios com maior potencial de crescimento que são de suma importância para o crescimento económico do país e do desenvolvimento. Fornecemos igualmente uma comparação entre as empresas portuguesas e algumas das suas congéneres europeias. Este contraste evidencia algumas diferenças, como um mais fraco desempenho, associado às framework conditions existentes a nível nacional, designadamente que diz respeito ao ambiente regulatório e cultural que estas empresas enfrentam. Palavras-chave: Demografia Empresarial, Framework conditions, Gazelas, Empresas de Elevado Crescimento, Portugal INTRODUCTION The emergence and proportion of high-growth and gazelle firms provides a clear indication of how well individual countries are laying the foundations for growth among new and existing businesses. These high-growth firms are known to play an important role in job creation, fostering innovative behaviour, enhancing productivity and are key players in economic growth (OECD, 2002). During the last decade, high-growth firms have attracted considerable attention from researchers, policymakers and more recently also from practitioners. The Portuguese economy experienced a process of structural transformation during the latter part of the twentieth century, which culminated in rapid economic expansion in the second half of the 1990s, followed by a deceleration of economic growth since 2001, contributing to a considerable creative destruction of industries, which has consequently impacted on firm dynamics. The emergence of high-growth firms can be a statement of the capacity of the country to deliver the right conditions for producing dynamic and rapidly growing firms. This paper characterises Portuguese high-growth and gazelle employer enterprises for the period and discusses performance and employment, by size, region and sector. Two parallel perspectives are provided, defined by a turnover and an employment criteria.

40 No internationally accepted definition exists of what constitutes a high-growth firm (Herenkson and Johansson, 2008, 2009). The literature offers several definitions inspired by the work of David Birch (1987). We will use the methodology by Eurostat and OECD (2007), which has been internationally accepted and used widely in the business demography field (OECD, 2008 and 2009). This paper is organised as follows. The next section intent is to describe the data and methodology applied to our data. Once the main features regarding the dynamics of high-growth and gazelle firms in Portugal are introduced, concerning their distribution, employment and size (section 3), a sectoral and regional disaggregation is provided in sections 4 and 5, respectively. In the following section, we offer a brief note on high-growth and gazelles firm survival, which is then followed by international data comparisons between Portuguese firms and its European counterparts. This comparison enables to explore the differences in national framework conditions in section 7 and conclude that the regulatory environment in which Portuguese firms operate seem to be instrumental to the explanation of their poorer performance. Finally, section 8 offers concluding remarks. 2. DATA AND METHODOLOGY The main data source in Portugal for the universe of employer enterprises (enterprises with more than one employee) is Quadros de Pessoal. This annual mandatory survey, conducted by the Portuguese Ministry of Labour and Social Security 16, provides a rich and comprehensive matched employer-employee-establishment dataset. According to the registrars of the Portuguese Social Security, it is composed of all active enterprises with at least one paid employee during the period 1985 to The database obtained from the cleaning of Quadros de Pessoal, adheres to the Eurostat and OECD methodology Manual on Business Demography Statistics (Eurostat/OECD, 2007). Experience has shown that the scope and methodology of business demography statistics have a significant impact on results (Eurostat, 2008). The Eurostat/OECD (2007) methodology is being widely (e.g. OECD, 2008, 2009; Eurostat, 2008; NESTA, 2009a and 2009b) used and allows for easier international comparisons within Europe, but also with the US. It focuses on employer enterprises, which are an important source of job creation. The derived dataset from the application of this methodology consists of an annual average of active employer enterprises, with an annual average of births and enterprise deaths over the period Although the dataset covers the period 1985 to 2009, two years at the beginning and end of the period are lost due to the application of the Eurostat/OCDE s (2007) methodology, when calculating enterprise births 17 and deaths 18. It is recommended looking two years into the past from the reference period, to check for reactivations, before enterprise births are actually considered (Eurostat/OECD, 2007). Thus, births were only calculated from 1987 onwards, instead of 1985, the starting year of the dataset (Figure 1). 16 Gabinete de Estratégia e Planeamento, Ministério do Trabalho e da Segurança Social. 17 According to the Eurostat and OECD definition, the core measure of births reflects the concept of employer enterprise birth. It corresponds to the birth of an enterprise with at least one employee. This population consists of enterprise births that have at least one employee in the birth year and of enterprises that existed before the year in consideration, but were below the threshold of one employee. A birth occurs when an enterprise starts activity. Births do not include entries into the population which result from break-ups, spit-offs, mergers, restructuring of enterprises or reactivations of units which are dormant within a period of two years. Births do not include reactivations of units which are dormant within a period of two years. Thus, this population consists of enterprises that have at least one paid employee in its birth year and also of enterprises that, despite existing before the year in consideration, were below the one employee threshold. An employer enterprise birth is thus counted in the dataset as a birth of an employer enterprise after it recruits its first employee, while complying with the above mentioned requisites. 18 A death can occur because the enterprise ceases to trade or because it shrinks below the one employee threshold. The Eurostat/OECD (2007) manual recommends waiting for two years after the reference period to allow for reactivations, before deaths are calculated. Deaths do not include exits from the population due to mergers, take-overs, break-ups or restructuring of a set of enterprises. Moreover, deaths do not include exits from a sub-population if it results from a change of activity. 39

41 Initial year of sample Calculation of firm births Calculation of HG enterprises Calculation of Gazelles End of calculation of HG and Gazelles Final year of sample gap of 2 years: to check of reactivations in enterprise births gap of 2 years: to check of reactivations in enterprise deaths gap of 3 years: to allow the count of annual average growth over a 3 year period for HG enterprieses, excluding first year newborns gap of 5 years: to allow the count of annual average growth over a 3 year period for enterprises born up to 5 years before, excluding first year newborns Figure 1: The application of the methodology and the timings required for the calculation of highgrowth and gazelle firms Source: Own elaboration. 40 A high-growth enterprise is any employer enterprise with 10 or more employees in the beginning of the observation period, with average annualised growth greater than 20% per annum, over a three year period. Growth can be measured according to two distinct definitions, either by the number of employees or by turnover (Figure 2). Given the time period of the dataset, the calculation of the number of high-growth firms was only possible after 1990, 3 years after 1987, where enterprise births could start being calculated. To fully comply with the methodology, growth rates have to be always identified from the same base population, thus excluding enterprises born in the first year from the growth measurement. Consequently, the data on high-growth enterprises should be cleaned so as to remove firms that were born in year t-3 (in our case, 1987), when measuring growth from t-3 to t. Gazelle enterprises are a subset of high-growth enterprises. Gazelles, measured by employment (or turnover), are all employer enterprises employing at least 10 employees in the beginning of the 3 years period, which have been employers for a period up to 5 years, with an annual average growth in employment (or turnover) greater than or equal to 20%, over a 3 year period. In other works, they reflect high-growth enterprises born 5 years or less before the end of the 3 year observation period. Moreover, the data on gazelles should also be cleaned by removing firms that were born in year t-5, when measuring growth from t-5 to t. A size threshold of 10 employees, for both turnover and employment, is set at the start of the observation period, to avoid the small size class bias contained in the above definition of high-growth and gazelles (Figure 2). In setting the employment threshold, the methodology needed to balance two competing criteria. If the threshold was set too low, it would cause a disproportionate number of small enterprises appearing in the statistics, but on the other hand, would reduce disclosure problems related to the statistical confidentiality of the microdata. If it was set too high, disclosure problems could increase, in particular for smaller economies where large enterprises are less numerous than smaller sized ones.

42 High-Growth Enterprises (as Measured by Turnover) Population of Enterprises with Ten or More Employees Gazelle Enterprises (as Measured by Turnover) Gazelle Enterprises (as Measured by Employment) High-Growth Enterprises (as Measured by Employment) Figure 2: Conceptualisation of the population of High-growth and Gazelles by turnover and employment criteria in the subset of enterprises with more than 10 employees Source: Own elaboration. The employment measurement of high-growth and gazelle firms is generally preferred and is more widely used (NESTA, 2009a; NESTA, 2011; NESTA, 2009b; OECD, 2002), as employment is a real variable, whereas turnover is a nominal variable, influenced by local and national economic factors like inflation and the structure of a country s fiscal system. Moreover, in our data, the turnover criteria show higher volatility than employment, when we account for both enterprises and employment in high-growth and gazelles. According to the OECD (2011), there are also greater discrepancies among countries when the turnover definition is used, particularly at the sectoral level. As referred, the dataset has been cleaned complying fully to the Eurostat/OECD (2007) methodology. Thus, we tried to identify and exclude mergers and acquisitions, when known, from the dataset. Thus, most of the growth reported here is mainly firm organic growth (growth through new appointments in a firm) and not to acquired growth (growth through acquisitions and/or mergers). Lastly, only employer enterprises classified in sectors from sections A to Q of the Portuguese Economic Classification of Economic Activities (CAE-Rev.2.1) were considered for the purposes of this research. 3. PROFILING OF HIGH-GROWTH AND GAZELLES IN PORTUGAL Before proceeding into a more detailed analysis of performance, it is useful to review some of the main characteristics of the population of high-growth and gazelles along this period. We will start by approaching its representativeness in overall employer enterprise population, according to both employment and turnover criteria for the calculation. 3.1 Overview of the data on high-growth and gazelles In 1985, employer enterprises with over 10 employees represented 27% of the total population and had 85,2% of all employment. In 2007, these shares dropped to 15,1% of all firms and 72,2% of all workers (the share on employment decreased relatively more). The application of the Eurostat/OECD (2007) methodology for the calculation of the amount of highgrowth and gazelle firms in any given year is exemplified in Figure 3, where the final year of 2007, is set as an example. Of the total amount of employer enterprises active in 2004 ( ), 3 years before the calculation year (2007), (16%) employed more than 10 employees. From this subset, there is a further selection of enterprises having an annual average growth of 20% over a 3 year period ( ), either by employment or by turnover. For the determination of Gazelles, 41

43 departing from the initial enterprise population, we isolate firms born five years or less before the end of the 3 year observation period ( ). From that subset, a further selection step takes place, whereby only young enterprises with over 10 employees remain. From this group, we apply the growth criteria in employment and turnover to obtain the final count for Gazelles. Tables 1 and 2 summarise the results obtained for high-growth and gazelles and their employment, according to both criteria employment and turnover criteria. In the 17 year period, ranging from 1990 to 2007, Portuguese high-growth firms and gazelles by turnover decreased both in number and employment (Table 1). However, when measured by employment, its number and employment increased for both High-growth and Gazelles. Employment in high-growth firms, by employment, was in fact the sole to register an increase in the share in total population, between 1990 and 2007 (7,4% to 7,6%) Total enterprises Enterprises > 10 employees (15,68%) High-growth enterprises (by employment) (3,3%) High-growth enterprises (by turnover) (10,4%) Young enterprises (21%) Young enterprises > 10 employees (7,6%) Gazelles (by employment) 363 (7,3%) Gazelles (by turnover) (23,7%) Figure 3: High-growth Enterprises and Gazelles, outlook for 2007 Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. In 1990, there were high-growth firms by turnover and according to the employment criteria (24,6% and 4,2% of the enterprises with over 10 employees, respectively). By 2007, the number of high-growth firms by turnover decreased 40% (to 9,5% of the population considered), while those by employment definition, increased around 10% (to 3% of the population). The number of gazelles by turnover is also higher than when measured by turnover. In 1992, reported gazelles were and 420, by turnover and employment, respectively. Gazelles by turnover also faced a considerable decrease relatively to 2007 (-31%), although not as high as highgrowth firms, as well as Gazelles by employment definition, which decreased by around 14%. Gazelles (employment definition) represent 23% of high-growth firms in 2007 and 34% in Table 1: High-growth enterprises, employment and turnover definition (number and % in employer enterprises with more than 10 employees) 42

45 there were relatively more high-growth firms accounted by the employment criteria than 17 years ago, although this is due to a particular rise of its number in 2007 (and not verified for the previous years). Its share on the population of firms with more than 10 employees has however decreased when compared with 1990 (3%), although keeping a somehow stable performance since The fact that the growth in contracting employees might accompany the growth in turnover more closely than in the past has still to be looked at in this research 19. While the decreasing share of high-growth firms (turnover definition) in the population is sustained throughout the whole period (24,6% to 9,5%), (Figure 7), the pattern of high-growth firms by employment shows considerable variation over time (Figure 6). The worst performing period is the aftermath of the 1993 crisis where economic conditions deteriorated severely, due to a widespread international economic crisis and speculative currency attacks within the European Monetary System. In 1993, Portugal s GDP growth was negative. Firms with over fifty employees were particularly hit (Sarmento and Nunes, 2012a). In the following year, the overall rate of growth in the number of employer enterprise firms was the highest of all the period, in particular for the size class of over 250 employees, coinciding with the start of the second community support framework (QCAII). However, the amount of firms verifying such an amount of growth as required either to be considered a high-growth or gazelle kept on decreasing until 1995 (by the employment definition) and 1996 (by the turnover definition). In fact, 1995 is the only year where the number of high-growth dropped below the threshold and gazelles below It is precisely 3 years after the peak of the crisis in That is, during the following 3 years, many firms could not add up an annualised average growth of 20% (in employment and in turnover) and thus classify in these categories. Gazelles are in a similar circumstance, but they seem to perform the worst during the three year period of 1994 to 1996, dropping below 300 according to the employment criteria. During the last half of the 1990s, general economic conditions were more favourable, and Portugal experienced a period of economic growth. High-growth firms, as well as gazelles, showed a sustained growth path in both number and amount of employees. The start of the millennium brought about a period of economic deterioration which contributed to the slowdown in Portuguese domestic demand, leading to a sharp deceleration of activity. The readjustment process of balance sheets among households and firms in order to correct economic imbalances was partly related to general cyclical developments in the European economy but also to downward adjustment of expenditure patterns, bringing spending more in line with incomes and revenues. Although this coincided largely to what has happening in the European Union (EU) economy at large, the amplitude of the downsizing was more pronounced in Portugal. Noticeably, the number of high-growth firms started to decrease after 2001, as they were not able to sustain such a rhythm of growth (a high-firm in 2001 had to sustain 20% growth p.a. from ), and most probably could have been excluded due to not complying with the growth requirement following In terms of employment, this is revealed only after 2002, perhaps because in 2000 firms had not yet adjustment employment to the new macroeconomic conditions Employment A number of studies maintain that high-growth firms account for a disproportionately large part of net job creation (Schreyer, 2000; OECD, 2002; NESTA, 2009a and 2009b). It is also well documented in the literature the disproportionate contribution of young and small firms to the generation of employment and earnings, to productivity growth and thus to overall wealth creation (Storey, 1994; Birch et al., 1995; Acs and Mueller, 2008; Praag and Versloot, 2008; Henrekson and Johanson, 2010). 19 Some evidence has pointed out that growth is first consummated in terms of turnover and only later on feeds into employment (from the nominal to the real side the economy). From the visible fluctuations of our data, we have no account of that phenomenon, but it is an issue worth looking at in subsequent work.

46 In this section, we will approach high-growth and gazelle employment, making use of an array of indicators, common to these types of studies. For the count of job creation several distinctions have to be made, namely flows of gross job creations and losses must be distinguished from net job creation (the difference between job gains and job losses). Although obtaining net job creation is commonly the target, information on gross flows can also be of interest to policy, as simultaneous job creation and destruction shows evidence of labour market churning, which is part of the dynamic process of market adjustment. Secondly, in net job creation, measures are needed which reflect the aggregate level, but also the relative importance of firm characteristics, and the role played by groups of firms, as net job creation may differ substantially across levels and collections of firms. For instance, even though total employment may decrease, certain groups of firms (e.g. large ones) may enjoy net job growth. One of the most common measures is net job creation rates for different firm characteristics, notably different size classes to account for the contribution of small and large firms. Thirdly, net job creation rates are percentage ratios relating net job gains to the total number employees 20. However, a large job creation rate does not necessarily mean a large absolute contribution to the total number of net jobs created 21. Thus, a size class with a small share of initial employment but on the other hand which displays a high net job creation rate, may still have a minor impact on overall job creation, whereas a size class that accounts for a large share of employment may contribute more substantially to overall net job creation, even with a small rate of net job creation. It might be useful to consider more in detail the way in which high-growth firms are measured in the methodology we adopted. In this paper, we are not measuring job creation in 3 year spans and thus accounting for employment growth that each single firm had from its first to its third year of growth. Clearly, this count will most often be positive and large, as the best performing firms are being measured precisely during the periods they perform the best, leading to the conclusion that highgrowth firms are responsible for a much greater share in employment than in the total amount of enterprises. In our methodology, as reported in the methodology section, firms have to comply with a 3 year annualised average growth (in either employment or turnover) of 20%, in order to be classified as a high-growth and this type of measurement in repeated each year. After being classified as a highgrowth in a given year, if in the following year that particular firm does not add up to that amount of annualised growth in the previous 3 years, it is removed from the group of high-growth firms. When a given firm leaves this group, it takes away its employees, representing a king of job destruction, which will only be cancelled out if newly incoming high-growth firms that same year bring along an equivalent amount of employees. Thus, net job creation might be negative in a given year, if the amount of employment of excluded high-growth firms (that are not able to sustain that amount of averaged growth that year) is greater than the amount of employment of incoming firms (that were included that year in the new count of high-growth firms). It can also happen if the outgoing firms are on average larger employers than incoming firms. So net job creation results from the combination of the quantity of firms entering and leaving this group of firms each year, with the amount of employment they bring along. Thus, with such an indicator as net job creation, we can have periods of negative job creation, whereby there is an outflow of high-growth firms with a larger average employment than the group of incoming firms. The debate concerning whether it is the rapid growth of a few firms, or the entry of many new firms, that generates employment growth is still being fuelled by new evidence for high-growth firms (Storey, 1994; Davidsson and Delmar, 2006). Henrekson and Johanson (2010) point to a complementarity between these two views, in the framework of gazelles, where employment in the average new firm is as important as the net job contribution of these firms In our case, in employer enterprises with more than 10 employees. 21 As absolute contributions are the product of net job creation rates and the share that a category occupies in total employment.

47 Nº In Figure 4, we portray net job creation in high-growth and gazelle firms (by employment definition) as compared to that of total economy. We observe it accompanies the major cycles of total economy, but its peaks are more softened, especially for gazelles that seem to suffer from a smaller volatility. Between 1996 and 2001, high-growth net job creation was jobs ( for total economy), in comparison to the net destruction jobs in the period comprehended between 2002 and 2006 ( for total economy). In this study we do not provide an account of job creation rates as considered in other publications. The type of calculation of net job creation rates provided by research such as FORA and NESTA s (2008) and NESTA (2009a and 2009b) can be misleading, also given the conclusions and the kind of policy advice they provide. High-growth firms are said to be responsible for most of the job creation in an economy. But this is fact happens due to the way job creation is measured. These studies measure job creation within 3 year spells, accounting for job creation growth as the measurement between the first and third year of this spell for firms which were already selected precisely because they were already growing. It is then that obvious that job creation has to be positive as no job destruction is accounted for when they leave the group of high-growth firms, and substantial, especially if they are composed by large firms Net job creation in HG firms Net job creation Gazelles Net job creation in active enterprises Figure 4: Net job creation in high-growth firms and gazelles (employment definition) and in active enterprises with over 10 employees (nº), Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Note: Job creation here is given by net job creation (difference between job gains and job losses) in any given year. 46 Large high-growth firms generate more than half of employment, on average throughout the period, whereas firms from the size class only have a share of 16% (Figure 5). We can observe that high-growth enterprises with and employees tend to decrease their share in total highgrowth employment to large firms (+10 p.p. from 1990 to 2007). Gazelles distribution of employment among different size classes portray a larger volatility over time (Figure 6). Firms in the largest size class have over 50% of employment in most years, but they only average 45% over the period. However, gazelles employment in the and size class in particular, is relatively larger than that of high-growth firms.

48 Figure 5: Share of high-growth employment in high -growth enterprises (employment definition), by size class Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Figure 6: Share of employment in gazelles (employment definition), by size class Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. 47

49 Average size of HG and Gazelles Average size of active pop, births and deaths ATAS/PROCCEDINGS 17º WORKSHOP APDR ISBN Firm dimension During the last twenty years, Portugal has experienced a growing number of smaller sized enterprises and a decrease of firm size across all main sectors, for both observed entrants and exiters in the market (Sarmento and Nunes, 2012b). Although the increasing predominance of small firms in the enterprise population is not unique to Portugal (Eurostat, 2009; OECD, 2008; Cabral, 2007; Bartelsman et al., 2005), the country presents sharp evidence of what can be roughly called as a smaller firm dominance trend. In fact, this is observed for enterprise births and deaths, for all regions, broad sectors and most size classes (Sarmento and Nunes, 2012b). The combination of firm demography factors, such as the smaller nascent firms, with structural effects, such as a service sector dominance (whose firms have a smaller average size than manufacturing), has caused an overall decline in average firm size, reflecting the influence of specialization effects towards industries with a smaller efficient scale, but particularly of within sector effects. Sarmento and Nunes (2012b) shifht-share decomposition of the determinants of firm size reveal that within sector effects have played a more important role in explainning differences in firm size across the period in Portugal. Even controlling for sectoral specialisation, intrinsic characteristics of sectors seem to be a fundamental determinant of its size structure. High-growth and gazelle firms have kept an average size significantly higher than the average employer enterprise in Portugal, whose size decreased consistently over time, from 16 employees in 1990 to 9 in 2007 (Figure 7). In the aftermath of the 1993 economic crisis, the average high-growth and gazelle firms still managed to increase in size up to 1999 (92 to 131 and 66 to 117 employees, respectively). But this was not sustained when the macroeconomic conditions deteriorated during the following years. From 2000, the slowdown in overall business demography dynamics in Portugal is rather compelling (Sarmento and Nunes, 2012a) and this also shows in gazelle and high-growth dynamics and average size evolution over the period. Notwithstanding the unfavourable economic conditions during the 2000s, a high-growth firm averaged 110 employees and a gazelle 94 in 2007, a higher size than at the start of the 1990s. The average high-growth firm is also larger than a gazelle by 18 employees, on average, throughout the period. Average Firm Size HG and Total population of firms Average HG enterprise size - by employment (nº HG emp/nº HG ent) Average firm size active pop firms (employment) Average Gaz enterprise size - by employment (nº G emp/nº G ent) Average firm size births (employment) 140 Average firm size deaths (employment) 130, ,7 92,5 15,1 96,6 14,3 89,7 92,0 67,9 66,0 106,9 12,0 119,1 118,9 114,4 113,8 105,2 116,7 103,5 100,0 95,9 11,6 11,3 11,0 10,8 10,6 121,2 113,3 102,1 102,1 10,0 10,0 116,7 101,1 104,3 109,1 109,88 101,3 97,8 101,8 93,66 81,2 86,3 9,4 9,3 9,3 9,0 9,0 9,

50 Average firm size ATAS/PROCCEDINGS 17º WORKSHOP APDR ISBN Figure 7: Average firm size of high-growth and gazelle firms (employment definition) and total active population, births and deaths of employer enterprises Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. According to Figure 8, there is little variation in the share of high-growth firms by firm size, except for the largest size class, where firm size seems to have been decreasing since Average size High-growth (employment) by size class to to to or more Figure 8: Average firm size of high-growth enterprises (employment definition) by size class Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Three-fifths (58%) of high-growth firms (employment definition) employed fewer than 50 employees on average during the period, with 3% employing between 10 and 19 people (Figure 9). More than half of high-growth firms (employment definition) belong to the size class, whereas only 7% (on average over the period) are large firms. When we calculate the proportion of high-growth firms in each employment size category we find very little variation over time, beyond some small fluctuations related to the business cycle (it peaks in 2000 and decreases from then on). The same happens with gazelles though the variance is greater in the greatest size class (+250). There is therefore little variation in the share of high-growth firms by firm size. 49

51 Figure 9: Share of high-growth enterprises (employment definition), by size class Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Similarly to high-growth, the majority of gazelles are SMEs, belonging to the 20 to 49 employees size class. A smaller share though, 6% on average throughout the period, are large firms. The composition of size-classes for gazelles is also stable over time (Figure 10). Figure 10: Share of Gazelles (employment definition), by size class Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Enterprises in general exhibit large heterogeneity in size, industry affiliation and realized growth performance. In the following sections, we will approach these dimensions for high-growth and gazelle firms HIGH-GROWTH AND GAZELLES BY INDUSTRY AFFILIATION In Portugal, the industrial structure is still segmented and characterized by a large primary sector, despite the existence of a number of modern productive sectors. The country still exhibits a considerable dichotomy between a minority of small scale modern activities, which are highly productive and a majority of low skill and low productivity sectors. There is also substantial degree of heterogeneity between firms, even within the same sectors (OECD, 2010; Sarmento and Nunes, 2012b). Over the period , industrial structure has evolved towards the reinforcement of the service sector in the economy and the decline of the manufacturing sector. According to Quadros de Pessoal dataset, the service sector leads both in the number, share of employer enterprises and employment, mainly after In 2006, the service sector was responsible for 72% of all new ventures. Moreover, 62% of total employment was generated by start-ups in services, which is higher than service sector s share in total employment (Sarmento and Nunes, 2012a). As seen before, the application of the two different measurement definitions (employment and turnover) for the calculation of high-growth and gazelle firms often leads to different results and a note should be specifically made on the sectoral approach. Services tend to be, by their own nature, more prevalent if considered according to the employment criteria, as they are relatively more

52 labour intensive, whereas manufacturing tends to be more enhanced when considered through the perspective of the turnover definition. In Portugal, high-growth firms and gazelles are emerging considerably more in service and commerce sectors. In Figures 11 and 12, where they are portrayed according to the employment criteria, we observe a clear shift in the distribution of high-growth firms over the period of analysis, away from manufacturing (34% in 1995, down to 20% in 2007) to services and commerce (49% in 1995 up to 56% in 2007), as well as construction (15% in 1995, up to 20% in 2007). A similar pattern is observed for gazelles, although the drop in manufacturing sector is higher, it decreases by almost a half in 13 years (42% in 1995 to 20% in 2007). A significant number of high-growth firms in Portugal operate in the construction sector, which has been particularly hit by variations in the business cycle. This sectoral rebalancing reflects trends already existing for the overall population of employer enterprises (Sarmento and Nunes, 2012a) and are not totally new to other European countries, even if at a lesser extent Agriculture and Fishery Construction Manufacturing Services and Commerce Figure 11: Share of high-growth enterprises (employment definition), % Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Note: One letter level of the statistical classification, NACE, Rev Agriculture and Fishery Construction Manufacturing Services and Commerce Figure 12: Share of Gazelle Enterprises (employment definition), % Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Note: One letter level of the statistical classification, NACE, Rev Comparing sectoral employment in and 2007 in detail, we observe that general high-growth firms employment in production activities, such as agriculture and manufacturing decrease their share in employment, construction increases slightly (11,3% in 2007) and services in general, and real estate in particular, increase considerably (49,2% to 65,7% and 14,6% to 38,3% from 1995 to 2007, respectively). Financial services, which only represented 1,6% of total employment in 1995 increased more than threefold to 5,4%, by 2007 (Figure 13) In analyzing the sector dimension, we only take into account the period from 1995 to This has to do with the start of European System of Accounts (ESA) in 1995 and to compatibility issues introduced by the new Portuguese Classification of Economic Activities Revision 3, implemented in 2007.

53 % 70 Sector share in HG employment, 1995 and , , ,3 34,0 38, , ,9 Production (10-41) 14,5 Manufacturing (15-37) 10,8 11,3 Construction (45) 14,8 Wholesale and retail trade (50-52) 4,4 5,4 3,8 2,53,5 1,6 Hotels and restaurants (55) Transport, storage (60-64) Financial intermediation (65-67) Figure 13: Sector decomposition of employment in high-growth firms (by employment) Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. 14,6 Real Estate (70-74) 3,5 1,7 Personel Services (50- service (90-99) 74) 4.1. Knowledge Intensive Business Services Knowledge Intensive Business Services (KIBS) are a subset of business services which have in general displayed more rapid and sustained growth rates than those of other economic sectors (Mamede et al., 2007; Arundel et al., 2007). Based on a larger contribution of services to the economy, KIBS are likely to become one of the main factors of potential growth within the European Union (EU). Its economic weight has been increasing in many countries (EC, 2007) and its role in value added, employment and even trade has therefore come under scrutiny from national authorities. Its importance grows as they become increasingly influential sources for the generation of new knowledge, the performance of organisations that are their clients and for the dynamism of the whole economy. But KIBS 23 have attracted policy interest not only because of its rapid rates of growth, but mostly because they are a highly innovative sub-sector within services, especially due to its role as inductors of innovation of firms in other sectors and as facilitators of the innovation process in the economy (Kubota, 2009; Arundel et al., 2007; Howells, 2006; Hertog, 2000; Hargadon, 1998; Miles et al., 1995). KIBS are also considered to contribute positively to the increase in productivity (Katsoulacos and Tsounis, 2000), through their three main interaction functions, as facilitators, carriers and sources (Kubota, 2009). Furthermore, their almost symbiotic relationship with client firms, often converts them into co-producers of innovation (Hertog, 2000). Despite their higher growth and better survival performance, most knowledge-based firms in Portugal do not qualify either as high-growth or gazelles. High-growth KIBS firms are less than 2% of total KIBS (by turnover) and less than 1,2% (employment definition). Gazelle KIBS present more volatility throughout time and correspond to a lower amount of total KIBS (below 1% over the period 1995 to 2006 in both turnover and employment terms) KIBS firms may show similar innovation patterns with manufacturing firms. This is supported by the fact that the R&D intensity of this type of services is above the average of manufacturing companies (EC, 2007).

55 1995= =100 This analysis also reveals that knowledge-intensive high-growth firms have been performing better than both the broad service sector and the overall population of high-growth enterprises in terms of the rate of growth of employment and number of firms (Figures 16 and 17). Employment definition 370 KIBS Service Sector Total HG enterprises ,6 296,9 320,3 278,1 289,1 315, ,3 240, ,9 154,6 208,2 171,2 221,5 192,7 191,8 173,0 166,5 144,5 159,1 159,1 138,0 141,2 173,5 154, , Figure 16: Growth of high-growth employer enterprises (employment definition), disaggregated into KIBS, service sector and total high-growth enterprises (1995=100) Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Note: KIBS are defined as NACE 72 to 74. When comparing employment in KIBS with other sectors over time, we again verify its better faster growth rate, especially relatively to manufacturing, which shows a sustained decrease over time. 450 Employment in HG, Services and Manufacturing (1995=100) KIBS Services Manufacturing ALL Figure 17: Growth of employment in high-growth employer enterprises (employment definition), disaggregated into KIBS, service and manufacturing sector and total high-growth enterprises (1995=100) Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Note: KIBS are defined as NACE 72 to 74.

56 5. REGIONAL OUTLOOK In this section, we will examine the regional distribution of high-growth and gazelle enterprises by NUT II regions. We find high-growth firms in every region of Portugal, but to different degrees. In absolute terms, the region which concentrates over 46% of high-growth firms in 2007 (by both criteria), is the capital region of Lisbon. Over time, both high-growth and gazelles have become more concentrated in the Lisbon area, becoming less represented in almost every Portuguese NUT II region, in particular in Centro, Algarve, and Alentejo (Figures 19 and 20). This contrast becomes sharper when the employment definition is used. The north/south dichotomy seems to have widened over time (Figure 18). 0 a a > Figure 18: Number of employees in high-growth firms according to the employment criteria, by NUTII (1997, 2004 e 2007) Source: Own elaboration. In 1990, the share of high-growth firms in the NUT II region of Lisbon, according to the employment definition (41,8%) was close, though smaller, of that accounted for with the turnover criteria (42,2%). After 17 years, this gap widens substantially and the high-growth count with the employment definition is now 8,5 p.p. larger, indicating that in Lisbon there are comparatively more firms growing faster in employment than in turnover. This might be caused by increasing specialisation in services, which causes high-growth firms to become relatively more employment intensive. Lisbon concentrates various public sector administrations and is particularly intensive in services such as financial and real estate activities. Furthermore, we must have caution in interpreting these results, as this might also be due substantially to the fact that a considerable amount of firms headquarters are located in the Lisbon area. On the other hand, the loss of prevalence of high-growth firms in the Norte region is quite clear. In 1990, 33% of high-growth firms (by employment) and a greater amount by turnover (35,8%), emerged in this region due to the predominance of manufacturing activities (the turnover definition tends to heighten the manufacturing sector). In 2007, after 17 years, Norte had lost 6,2 p.p. of its high-growth firms according to the employment criteria, and more in terms of turnover (-6,6 p.p.), 55

57 % ending up with a total share of 27% and 29,2% (Figures 19 and 20). More peripheral regions, such as the Archipelagos of Madeira and Açores, have not experienced significant changes in the share of high-growth firms between 1990 and 2007 when accounted to the employment definition, although with the turnover criteria they both increase their high-growth share by 1,2 p.p.. Distribution of HG (empl) employment by NUT II regions , , ,8 33,1 32, , Lisboa e Vale do Tejo 15,8 12,7 11,4 4,4 3,0 2,2 2,9 1,9 2,6 1,1 1,4 1,1 0,9 1,5 1,1 Norte Centro Alentejo Algarve Madeira Açores Figure 19: Distribution of high-growth firms (employment criteria) by NUTII regions Source: Own calculations, based on "Quadros de Pessoal", GEP, Ministério do Trabalho e da Solidariedade Social and the OECD/Eurostat s (2007) methodology. Now turning to gazelles, an immediately visible fact is that Norte lost the prevalence as the region with most gazelles in the country at the start of the 1990s (Figure 21). In 1992, Norte had 44,3% of gazelles according to employment definition and 52,3% according to the turnover. By 2000, this share reduced to a quarter, by the employment criteria, and to 35,2%, according to the turnover definition. After EU s accession in 1986, the manufacturing sector, in which Norte is specialised, was severely affected by the restructuring of many firms. By 2007, there are signs of a slight recovery in this regions quota of gazelles. Another aspect worth highlighting is that the share lost by the Norte, seems to have moved south, to the Lisbon area, where the share of the employment criteria surpasses that of turnover, indicating a higher concentration of services in this region (56,1% by employment and 39,3% by turnover, a 16,8 p.p. of difference, twice as much as in high-growth firms). Algarve is the sole region that manages to recover slightly its share of gazelles in 2007 (2,6%, by employment), whereas Centro faces loses throughout the period. 56

60 average size of employer enterprises and the servicisation of the economy (Sarmento and Nunes, 2012a and 2012b). In Portugal, the estimated median duration of a new born enterprise lies between 5 and 6 years (Nunes and Sarmento, 2012), while in other countries, such as the US, UK, Germany, Italy, and Spain the maximum of the unconditional hazard function is reached before the sixth year (Wagner, 1994; Audretsch et al., 1999; Bartelsman et al., 2005 ; Bhattacharjee, 2005 ; López-Garcia and Puente, 2006). It is noticeable that Portuguese firms keep on failing for a longer period, before the hazard rate 24 starts declining. Firm s size has been found of extreme importance in determining the probability of survival of Portuguese firms (Geroski et al., 2010; Mata and Portugal, 1994). There is a significant relationship between size and chance of survival particularly for new start-ups, who face the greatest uncertainty regarding market conditions and for firms in the service sector. Moreover, at the sectoral level firms in the construction sector exhibit the highest risk of failure, while firms in the service sector display the highest survival rates (Nunes and Sarmento, 2012). A full scale survival analysis for Portuguese high-growth firms has not been conducted yet. But for Spanish high-growth firms, there is evidence that being a start-up increases the probability of fast growth, as well as for firms with initial higher relative wages and debt ratio, up to a certain point (López-Garcia and Puente, 2011). However, the work conducted by the Bank of Portugal (BP, 2010), using a dataset known as Central de Balanços, which also complies with the Eurostat/OECDs (2007) methodology, shed some light on survival issues. Its dataset comprehends non-financial societies in 2009, where 87% of firms in are micro-firms 25 (84% in 2000), 99,7% are SME s 26 and 0,3% are large firms, responsible for 28% of employment and 41% of turnover. Around 11% of all firms are high-growth firms over the period (10% in 2009 and 13% in 2000). According to the Bank of Portugal s data, around a quarter of high-growth firms and gazelles died during the period 2000 to They find that the share of high-growth firms ceasing activity over the period decreases substantially as firm s size increase (Table 3). By economic sector, the highest records of firms closure 27 occurred in Commerce and secondly in the Manufacturing sector, while the sector of Electricity and Water records the lowest closure rates. Table 3: Share of high-growth and gazelles closed during by dimension, district, juridical nature and sector 24 The hazard rate measures the rate at which risk (in this case of a firms closure) is accumulated and can vary from zero (no risk at all) to infinity. 25 According to the Commission Recommendation 2003/361/EC of 6 May 2003 (Official Journal L124 of ), a microenterprise is defined as an enterprise which employs fewer than 10 persons and whose annual turnover and/or annual balance sheet total does not exceed EUR 2 million. 26 This definition includes micro-firms. 27 Firms that have left the dataset. 59

61 Structure High-Growth Gazelles Total Universe of non financial firms 23,8% 25,8% By dimension By district Microfirms 83,6% 23,6% 26,5% SMEs 16,2% 20,0% 21,9% Large firms 0,2% 14,6% 18,8% Lisbon 29,7% 25,9% 28,2% Porto 17,4% 24,6% 26,3% Other locations 52,9% 22,4% 24,3% By juridical nature Limited liability companies 94,3% 23,6% 25,5% Public limited companies 3,1% 17,7% 21,2% Other 2,6% 38,9% 43,9% Agriculture and Fisherires 2,7% 19,1% 22,2% Manufacturing 13,7% 26,6% 28,9% By sector Electricity and Water 0,3% 12,7% 14,7% Construction 14,8% 23,2% 25,5% Commerce 28,4% 28,4% 30,2% Other Services 40,1% 20,3% 21,8% Source: Banco de Portugal (2010), based on Central de Balanços database and the Eurostat/OECD s (2007) methodology. Bank of Portugal (2010) also looked into what was the maximum dimension attained by those enterprises, using the data for high-growth or gazelles firms that survived throughout 1991 to (Table X). Most microfirms maintained its dimension throughout their lifetime (86% of high-growth and 88% of gazelles) with a minority of firms reaching the status of SMEs (13,6% of high-growth and 12,2% of Gazelles). This growth is associated, according to the Bank of Portugal, to short-run factors associated with the business cycle, which do not grant an organic growth. They also mention that some firms closing down are due to acquisitions, although they are residual given the total amount of firms. From the 87% microfirms in the dataset, only 10 grew into large firms between 2000 and 2009 (Table 4). These results demonstrate that enjoying high growth does not necessarily grant better survival prospects in Portugal. Gazelles seem more prone to dying than high-growth enterprises, hinting at rapid growth, based on short-run factors not granting either longevity or sustained growth. Further What is the maximum size attained by work is still needs to be conducted to approach survival determinants for Portuguese high-growth HG firms and Gazelles throughout their firms and gazelles. life? Table 4: Maximum size attained by high-growth firms and gazelles throughout their life Microfirms: Dimension HG firms Gazelles Microfirms 86,4% 87,8% SMEs 13,6% 12,2% 60 Large firms 0,0% 0,0% Source: Banco de Portugal (2010), based on Central de Balanços database and the Eurostat/OECD s (2007) methodology. 7. INTERNATIONAL COMPARISONS 28 Bank of Portugal (2010), used the data of all high-growth or gazelles that were classified as such during some period of their lifes, between 1991 and 2009.

62 % ATAS/PROCCEDINGS 17º WORKSHOP APDR ISBN There is a considerable lack of internationally comparable data on high-growth firms, thus limited research exists as to how Portuguese high-growth firms compares to other countries. However, recent developments at the OECD, the academic community and government level, have made important advancements (OECD, 2008 and 2009; Bravo-Biosca, 2011; NESTA, 2009a and 2009b). One of the first papers cross-comparing high-growth firms across countries has been that of Hoffman and Jung (2006), who looked at 17 countries during three year periods, ending between 1999 and 2001, using Bureau van Dijk s Amadeus and Orbis database. These authors use a high-growth definition which is comparable to that of the OECD/Eurostat 29 (2007). In trying to harmonise the data among different countries, they only considered firms between 15 and 200 employees. According to Hoffman and Junge s (2006) research, Portugal has relatively few high-growth start-ups relatively to other countries. In 2001, among 17 countries, Portugal is ranked as the 16 th with the highest proportion of high-growth firms by turnover and 9 th by employment. The OECD Business Demography Statistics provides data on business demography that complies with the Eurostat/OECD (2007) methodology for high-growth firms only. Due to data breaks and methodological changes in the official business statistical series published by Statistics Portugal 30 (INE) and the requirements of the Eurostat/OECD (2007) methodology, data is only provided from High-growth enterprises represent for most countries a small share of the total population, typically between 3,5% and 6%, when measured by employment growth (OECD, 2011). There are greater discrepancies among countries when the turnover criteria is used, which can in some cases overcome 20% of the population of firms. For most countries, the usage of these different measurement criteria also brings about differences among sectors. When measured by employment criteria, high-growth firms are more predominant in services, but when measured by turnover their prevalence is higher in manufacturing. Portugal seems to rank slightly higher with the turnover than with the employment definition, as confirmed as well by our data, highlighting its relatively small-sized enterprise fabric. In 2007, Portugal ranked 14 th in the share of high-growth enterprises in manufacturing and 12 th in services (turnover definition), among 16 countries, while according to the employment definition it ranked 19 th in manufacturing and 16Figure th in services, 11.1 Share of among high-growth 21 enterprises countries (employment (Figure 24). definition) in 2007 Manufacturing Services According to Hoffman and Junge (2006), a high-growth firm is reported as a firm with a growth rate (in either employment or turnover) higher than 60% in the period from t to t+2. Moreover, they require a positive growth in both time periods of at least 20%. 30 Namely the introduction of Informação Empresarial Simplificada.

63 % Figure 24: Share of high-growth enterprises (employment definition) in 2007 Source: OECD (2011). Considering solely total industry without the construction sector (employment definition), we observe that Portugal displays a smaller amount (2,2%) of high-growth firms in 2007 than most countries, except that for Italy and eventually the Netherlands (Figure 25). However, considering construction in isolation, we observe that the share increases to 4,1% (Figure 26), indicating greater dynamics of employment growth in this sector, above the Services sector (4%). In fact, having a higher share of high-growth firms in construction than in total industry except construction is a common feature to most countries during these two year period, namely in the US, Slovenia, Canada, Spain, Sweden, Italy, Denmark and Norway ,2 5,7 5,8 5, ,6 4,6 3,8 3,4 4,0 3,4 3,3 2,9 2,2 2,0 2,0 3,2 2,9 2,6 1,9 1 0 Figure 25: Share of high-growth firms (employment definition) in total active population of employer enterprises, total industry except construction (NACE 10-41), ranked by 2007 Source: OECD SDBS - Business Demography Statistics ,1 11, ,8 7,9 6,8 6,4 6,4 4,4 4,8 5,4 5,2 5,4 5,2 5,0 4,0 4,1 4,4 4,1 3,53,6 3,2 1,7 1,4 1,51,6 3,7 3, Figure 26: Share of high-growth firms (employment definition) in total active population of employer enterprises, construction (NACE 45), ranked by 2007 Source: OECD SDBS - Business Demography Statistics. We not turn to the comparison of Portugal and the UK (NESTA, 2009a and 200b). In the UK, highgrowth firms accounted for 6,4% of the total population of UK firms during and 5,8%

64 % during 2005 and In particular during the period , the UK had one of the largest shares of high-growth firms among OECD economies (NESTA, 2009b). Considering the period , the number of high-growth firms in the UK are twice as much as in Portugal when accounted with the employment criteria, but when accounted by turnover definition, Portugal has 87% of the number of high-growth firms of the UK 31 (Figure 27). By comparing the UK period with the period , there is also evidence that only around half that share was generated in Portugal 32, according to the employment definition. Yet again, this underlines the relative small average size of Portuguese firms, which is particularly observed when the employment definition is used for international comparisons. 14 Portugal UK 12, ,9 9,6 9, ,4 5, ,8 2, * * By employment By turnover Figure 27: Share of high-growth firms in Portugal and in the UK Source: Own calculations, based on "Quadros de Pessoal", GEP/MTSS for Portugal and NESTA (2009b), based on OECD/Eurostat s (2007) methodology. Note: * For the UK the period is The average high-growth company in the UK tripled its employment over each three-year periods, and (2009a). Given the differences in the way employment is accounted for in our paper and in NESTA s research (NESTA, 2009a and 2009b), as we do not calculate employment growth for the same cohort in three year spells, we are only able to fully compare the year In 2005, UK high-growth firms generated 11,6% of total employment, compared to 4,4% of their Portuguese counterparts. This to not only shows that high-growth firms are relatively more abundant in the UK, but hints at the possibility of UK high-growth firms being larger than the Portuguese. In fact, the enterprise fabric in the UK is quite distinct from that of Portugal. For a start, the number of large firms in the UK is amongst the highest in Europe, comparable to the US, when adjustments are made to reflect variations in size of the economies (BERR, 2008, p. 15). In trying to confirm these size discrepancies, we not turn to the analysis of average high-growth firm size in In Portugal, they averaged 101 employees, while in the UK they employed more than twice this amount (235 employees). Table 5 compares average size of firms in year 2005 and 2008 (end of period) for the UK and 2005 and 2007 for Portugal, according to size class. Despite the different size disaggregation for the largest size classes, it is clear that for all size classes, UK firms display more than twice the size of Portuguese There is a sharp decrease in the turnover measurement for high-growth firms during the second period, , in the UK (NESTA 2009b). 32 Data for Portugal for the second period refers to while UK data refers to

65 firms. Even comparing within the (larger) size class for Portuguese firms of employees with the of UK firms, we notice that UK firms are three times larger in 2005 and twice as much when the last years of Table 5 are compared (2007 for Portugal and 2008 for the UK). This difference tends to widen as we move on to the largest size classes, where the size discrepancy gets enlarged. Table 5: Average firm size (employment definition) by size class, in Portugal and in the UK UK Portugal Average size in 2005 (end 3 year period) Average size in 2008 (end 3 year period) Average size in 2005 Average size in to to to All HG All HG firms firms Source: Own calculations, based on "Quadros de Pessoal", GEP/MTSS for Portugal and NESTA (2009b), based on OECD/Eurostat s (2007) methodology CONCLUSIONS The contributions of this paper to the literature is three-fold: firstly, it focuses on the profiling of high-growth and gazelle firms to a detail which has not been previously considered. The analysis carried out in this study is based on a comprehensive dataset which provides the platform for uncovering features of these firms which have not been examined to such a detail before. The results show that in 2007, only 9,5% of all Portuguese employer enterprises (with more than ten employees) have a turnover that is in line with that of high-growth firms. If instead of turnover, we consider the growth in the number of employees, then the percentage of high-growth firms drops by 6,5p.p. to just 3 %. In addition, firms that can be classified as gazelles constitute only 2,2% of the total number of Portuguese employer enterprises if turnover is the criteria considered and 0,7% if we focus on employment growth. These percentages are significantly lower than at the beginning of the period considered in this study. In 1985, 24,6% were high-growth firms, of which 3,7% were gazelles according to turnover and 4,2% and 1,1% respectively if we take into account employment growth instead. The analysis also provides evidence of a narrowing gap between the two measurement criteria, the ratio of high-growth firms according to turnover to the respective employment. A similar pattern has been observed for gazelle firms, indicating that more firms are now growing faster in employment than in turnover. Most high impact firms are SMEs, but it is the largest size class firms, over 250 employees, that accounts for most employment creation. This research also provides evidence of significant differences of high-growth firms across regions, with more than half concentrated around the area of Lisbon and a quarter in the North. Over the years, a pattern emerges whereby more and more high-growth firms become concentrated around the Lisbon area. It should also be noted that there has been an important shift in the distribution of both high-growth firms over the period of analysis, away from manufacturing to services and commerce, as well as construction. A similar pattern is observed for firms classified as gazelles. High-growth firms are not a homogeneous group of firms. Firm s growth differences in Portugal go beyond high-growth firms and are deeply rooted in the structure of Portuguese entrepreneurial fabric. High-growth is a stage in the development of firms with potential and ambition to grow.

66 Secondly, by establishing a comparison between Portuguese high-growth and gazelle firms and their European counterparts, we highlight fragilities in performance and assesses the role of framework conditions, particularly in the context of the current economic, social and financial climate. Portugal produces less high-growth firms than most European countries. This lower ability to generate high-growth firms in Portugal raises concerns about the incapacity to generate such a track of growth, highlighting the laggard growth dynamism across the Portuguese entrepreneurial fabric. The small size of these industries hinders growth and particularly survival in globalised markets. These have also been considered to be partly a reflection of the country s substantial educational gap (OECD, 2010). However, wider barriers to growth need to be removed in Portugal. This relates to addressing a whole array of structural factors that lay deep in the bottom of the country s culture and education. Entrepreneurial culture needs to be targeted with long term structural policies, so as to foster a more risk-taking individual attitude towards business. Thirdly, we conclude that the sort of overreaching structural problems the country faces means that policies should not be confined to specific kinds of firms, even if high-growth. This means addressing a whole range of framework conditions underlying overall firms poor performance in terms of growth and survival. Portugal still remains one of the poorest OECD countries (OECD, 2010, p. 9). The country is in need of policies which are effective at delivering productivity growth. In the short to medium term, there seems to be significant margins to increase productivity, if efficient firms are encouraged to entry the market, while inefficient firms are forced out, in particular in the services sector. Policies need to be more focused in providing the platform for entrepreneurial activities to thrive, particularly in those sectors earmarked as pivotal to economic growth. In practice, policymakers could assist in the process of firm creation and growth, by reducing red tape and bureaucracy, working with financial institutions towards easing access to credit and improving labour and product regulations. More generally, policymakers should contribute to the establishment of an environment which is appealing to venture capitalists and business angels, and which can be ultimately regarded as conducive to investing in the country. REFERENCES Acs, Z. J. and P. Mueller (2008), Employment effects of business dynamics: Mice, gazelles and elephants, Small Business Economics, 30(1), pp Arundel, A., M. Kanerva, A. van Cruysen and H. Hollanders (2007), Innovation statistics for the European Service Sector, UNU-MERIT, Innometrics 2007 thematic paper. Audretsch, D., E. Santarelli, and M. Vivarelli (1999), Start-up size and industrial dynamics: some evidence from Italian manufacturing, International Journal of Industrial Organization, Elsevier, Vol. 17, n. 7, pp Banco de Portugal (2010), Estrutura e dinâmica das Sociedades não financeiras em Portugal, Estudos da Central de Balanços, Dezembro. Bartelsman, E., S. Scarpetta and F. Schivardi (2005), Comparative analysis of firm demographics and survival: Evidence from micro-level sources in OECD countries, Industrial and Corporate Change, 14(3), pp Bhattacharjee, A. (2005), Models of Firm Dynamics and the Hazard Rate of Exits: Reconciling Theory and Evidence using Hazard Regression Models, Econometrics , EconWPA. Birch, David L. (1987), Job Creation in America: How Our Smallest Companies Put the Most People to Work, New York: Free Press. Birch, D. L., A. Haggerty and W. Parsons (1995), Who s creating jobs?, Boston, Cognetics Inc. Bravo-Biosca, A. (2011), A look at business growth and contraction in Europe, presented to the 3rd European Conference on Corporate R&D and Innovation CONCORD-2011, October 6th 2011, Spain. 65

68 OECD (2008), Measuring entrepreneurship: A digest of indicators, OECD-Eurostat Entrepreneurship Indicators Programme, OECD Statistics Directorate. OECD (2007), OECD Framework for the evaluation of SME and entrepreneurship policies and programmes, Paris. OECD (2002), High-growth SMEs and Employment, Paris. Sarmento, E. de Morais and A. Nunes (2012a), The dynamics of employer enterprise creation in Portugal over the last two decades: a size, regional and sectoral perspective, Notas Económicas. Sarmento, E. de Morais and A. Nunes (2012b), Getting smaller: size dynamics of employer enterprises in Portugal, in Bonnet, J. et al. (eds.) The Shift to the Entrepreneurial Society: A Built Economy in Education, Sustainability and Regulation, Edward Elgar (Chapter 17). Schreyer, P. (2000), High-growth Firms and Employment. OECD Science, Technology and Industry Working Papers, 2000/3, Paris. Storey, D. (1994), Understanding the small business sector, London, Routledge. van Praag, C. M. and Peter H. Versloot (2008), The Economic Benefits and Costs of Entrepreneurship: A Review of the Research, Foundations and Trends in Entrepreneurship, 4(2), pp Wagner, J. (1994), The Post-Entry Performance of new Small Firms in German Manufacturing Industries, The Journal of Industrial Economics XLII, 12, pp ACKNOWLEDGEMENTS The authors would like to thank Gabinete de Estratégia e Planeamento of the Portuguese Ministry of Labour and Social Security for the provision of the data. 67

111 CORPORATE R&D STRATEGY AND GROWTH OF US START- UPS: WHY MATTERS THE LICENSE-IN OF EXTERNAL PATENTS? Dina Pereira 1, João Leitão 2, Tessaleno Devezas 1 1 University of Beira Interior, Department of Management and Economics Covilhã, Portugal. Centre for Aerospace Science and Technologies (CAST ), Dept. Electromechanical Engineering, University of Beira Interior Covilhã, Portugal. 2University of Beira Interior, Department of Management and Economics Covilhã, Portugal. Centre for Management Studies of Instituto Superior Técnico, CEG-IST, Universidade Técnica de Lisboa, Av. Rovisco Pais, 1, Lisboa, Portugal. Abstract. The present paper intends to estimate the effects of determinants of start-ups growth based on a corporate R&D strategy characterized by the innovative intensity of the firm, using as proxies, the R&D intensity, the start-up s patent portfolio and the business models for patent management, e.g., license-in and license-out, by making use of a panel data approach. We control for the technological intensity through the NACE classification, being the purpose to focus on the hightech and medium high-tech start-ups. By using a two-step panel data model, static and dynamic estimations are performed among a sample of 818 firms created in 2004 and tracked by the Kauffman Foundation in the subsequent six years. The major results show a significant and positive impact of R&D intensity and the license-in of external patents on the start-ups growth and a negative and significant effect of the squared R&D intensity on the growth path of the firm, revealing a U- inverted relationship to firm s growth, a positive impact on firm growth in an early stage, succeeded by a negative after achieving the optimal level. These conclusions are also ratified when controlling the activity sector, having a major impact on sectors like high-tech manufacturing industries and high-tech knowledge intensive services. Key words: Firm s growth; Panel data; Patent management; R&D intensity. JEL Classification codes: L26; O Introduction In high-tech sectors the pace of technological change is commonly high and tends to shorten product s lifecycle. In this connection, and in order to avoid competition, which in this type of sector also tends to be extremely high, firms success can depend on their IP rights and on the early-mover effect. In innovation intensive industries, patents facilitate active, creative and tradable markets for technology. Also, the protection of knowledge through patents enables innovators to act as licensors and make their assets commercially available to licensees (Joshi & Nerkar, 2010). Helmers & Rogers (2011) argue that patents, allow inventors to exploit in a successful way, their inventions conferring firms a competitive advantage in terms of an increased performance when comparing to non-patenting firms. The paper makes an analysis of the theoretical background regarding the determinant factors of firm growth and additionally reviews the literature on corporate R&D strategy focusing on patenting as determinant for firm growth. This paper significantly differs from previous studies on one count. It employs corporate R&D strategy factors (such as R&D intensity, patent portfolio, business models for patent management, e.g. license-in and license-out of patents) that are directly connected with firm growth. The paper is outlined as follows. Section 2 develops the theoretical underpinnings, drawing from the literature on firm growth, reviewing the main firm growth theories, major factors of firm growth, uncovering the determinants based on R&D investment efforts analyzing the theoretical background on patents acting as determinants for firm growth. Section 3 presents the empirical approach and discusses the results. Section 4 concludes and provides policy implications as well as guidelines for

112 entrepreneurs and practitioners in the framework of technological entrepreneurship and firm growth based on corporate R&D strategy factors, especially the one related to business models for patent management. 2. Literature review and research hypotheses Firm growth is a topic that has been the target of several analyses in the literature from different approaches, due to its importance and relevance for firm survival, generation of employment, increased economic growth and dynamism as well as the industrial concentration of firms, the process of firm selection and competitiveness in the sequence of diverse efficiency levels, and the introduction of innovation and technological change (Suárez, 1999). According to Delmar (1997) and Ardishvili et al. (1998) there are several indicators when measuring firm growth, such as: the financial or stock market value; the number of employees; the total sales and revenues; the productivity; the value of production; and the gross value added. Kirchhoff & Norton (1992) used three measures denoting their interchangeability in the way that they produce the same set of results when tested in a period of seven years, namely employment, total assets and sales. Delmar et al. (2003) after analysing several measures defend that the use of different indicators has to do with the objectives of the investigation. He also poined out to some limitations of the measures. For instance sales, although easy to access, can be an unsatisfactory indicator since it can be biased by the firm's arbitrary decisions and strategies and in the sequence of vertical integration of the production processes, being also sensitive to currency exchange rates and inflation. Value added, although is capable of explaining the internal activity, is not publicly available and assets can loose explanatory capacity specially if applied to services. Authors like Penrose (1959) and Kimberley (1976) state that the number of employees can be a good indicator as they are suitable to explain organizational complexity and managerial implications of growth. Nevertheless, Delmar et al. (2003) defend that the use of number of employees doesn't reflect the strategic decisions of firms, such as labour productivity, technological change, labour processes and others. Scherer (1970) pointed to a set of factors that influence size and growth, such as the economies and diseconomies of scale, mergers and acquisitions, government policies, and stochastic determinants of market structure. Storey (1994) presented a classification based on three main groups of determinant factors for firm growth: the ones related to the entrepreneur; the ones that are concerned to the firm; and the ones associated with the corporate strategy. The first group takes into consideration the individual resources of the entrepreneur, such as motivation, unemployment, education, management experience, number of founders, prior self-employment, family history, social marginality, functional skills, training, age, prior business failures, prior sector experience, prior firm size experience and gender. The second group deals with age, sector, legal form, location, size and ownership. The third one has to do with measures like workforce and management training, external equity, technological sophistication, market positioning and adjustments, planning, new products, management recruitment, state support, customer concentration, competition, information and advice, and exporting. Accordingly to Storey (1994), firms can be divided in three main groups, the failures, the trundlers and the flyers. The first ones tend to exit after entering into the market. The second ones survive until the observed period but don't reflect change in size. The last ones are the firms responsible for net job creation and increase in size. Following Gibrat (1931), Mansfield (1962) and Audretsch et al. (2004) the so called Gibrat s Law, which is also known as the Law of Proportionate Effect, states that the growth rate of a firm is 111

113 independent of its size at the beginning of the examined period, being the probability of a proportionate change in size during a certain period the same for all firms in a specified industry, not denoting influence the size of the firm at the starting period under consideration. Taking the previous into account we hypothesize that: H 1 : The firm s growth has a negative and significant relationship with size. Several studies focused on the relationship between performance and corporate R&D (using R&D expenditures as a proxy) oriented to innovative activities and products. For instance Morbey & Reithner (1990) stated that the investment of firms on R&D is positively related to the firm growth and with the generation of knowledge flows needed for product and process innovation. In this sense, R&D activity is supposed to contribute to the success of firms that are dedicated to an innovative strategy. In the light of the theory of the resource-based view (Barney, 1991; Makadok, 2001) valuable, rare and inimitable resources can act as competitive advantage for firms in order to grow either, under a sustainable basis. Kumar & Siddharthan (1994) analyzed the positive relationship between the performance of low and medium technology industries and R&D expenditures. Geroski & Toker (1996) by analyzing a sample of 209 leading UK firms concluded that innovation has a significant positive relationship with sales growth. Roper (1997) makes use of a survey data on 2721 small UK, Irish and German firms in order to verify the positive effect of the introduction of innovative products by firms on sales growth. Most scholars in studies on growth and innovation used R&D intensity as a proxy for innovation. The R&D intensity refers to a firm s expenditure in new technology development and product innovation, by taking as reference the total sales (Li, 1999). Freel (2000), by studying 228 small UK manufacturing firms, concluded that innovators are likely to grow more rapidly than non-innovators. Nonetheless, when focusing on the pharmaceutical sector, Bottazzi et al. (2001) didn't found any significant effect of a firm s innovative behavior on sales growth. Del Monte & Papagni (2003) also verified a positive relationship between sales growth and R&D activity, when analyzing a sample of Italian manufacturing firms. Regarding the studies performed by Ural & Acaravci (2006), R&D for technological innovation has a central position in the definition of the business strategy for firms, especially in the selection of the competition mode. In accordance with Wiklund et al. (2010) and Anderson & Eshima (2011) the resources of a firm are of critical importance to the development of the firm s capacity to be innovative, proactive and assume a risk-taking behavior. In this vein, the possession by a firm of a set of intellectual property IP assets is an important factor that determines the ability to undertake strategies that result in positive outcomes. The authors defend that firms (and especially younger firms with less than 5 years) with more intangible resources are more prone to perform strategically in order to pursue opportunities that in the long-term generate higher sales. Thus: 112 H 2 : The firm s growth has a positive and significant relationship with R&D intensity. Despite the theoretical background on the positive relationship between firm growth and R&D intensity, several scholars defend that this is not always a linear relationship (Ittner & Larcker, 1998; Canibano et al., 2000; Luft & Shields, 2003). Penrose (1959) s growth theory also stated that firms aren't able to pursue an unlimited expansion line regarding R&D investments since they are constrained by managerial capacity limitations, being these investments responsible for non-positive

114 effects on operating performance. Similarly, Hitt et al. (1997) and Bharadwaj et al. (1999) found a negative impact of R&D investments on firm s performance. In addition, R&D investments can denote a positive impact on firm growth in an early stage, although it turns to be negative after achieving the optimal level. Regarding their study of Portuguese SME's, Serrasqueiro et al. (2010) denote that R&D intensity is an important determinant for the survival of firms, presenting a significant non-linearity over growth distribution. In this vein, they defend that Gibrat s Law cannot be rejected in the light of size reductions of the firms analyzed, being rejected when the firm size increases. R&D intensity is then considered by the authors as a restrictive determinant of the firm's growth when considering a reduction in size, acting as a catalyst for growth in the presence of an increased size. Thus: H 3 : R&D intensity of the firm has a U-inverted relationship with its growth. Cuervo (2005) states that if there is a market for almost everything, the firm s competitive advantage for growing can be based on its accumulated intangible assets (knowledge capital) either in the forms of brands, reputation, knowledge or in the form of decision and problem-solving systems, such as the organizational routines and the incentive systems. According to Baumol (1990) and Wennekers & Turik (1999) entrepreneurship and the process of new firm entry is a key aspect for economic development, contributing to economic growth through the generation, dissemination and exploitation of innovative ideas, enabling efficiency, productivity, increased competition and providing diversification among firms. Regarding the Helmers & Rogers (2011) work, by allowing inventors to profit from their inventions, patents are determinant to confer firms that own this kind of IP asset a competitive advantage conveying an improved performance and subsequent growth when comparing to non-patenting firms. Conversely the patent system makes efforts to motivate the creation of new firms based on inventions, relying on their patent assets to generate a share of the market and achieve additional revenues from their innovativeness. Thus, the patent system works to rectify the appropriability problem, especially when dealing with new and small firms. Start-ups that patent shall, therefore, be more successful than the non-patenting ones. In addition, Rosenbusch et al. (2011) also convey that there is a relationship between the SME s growth and an innovation-centric corporate strategy. Thus: H 4 : The firm's growth has a positive and significant relationship with the patent portfolio. Schneider & Veugelers (2010) call the attention for the importance of young and innovative firms fostering innovation and growth. The main obstacles for the few studies covering this topic are explained by Helmers & Rogers (2011), due to difficulties in capturing the effects of a patent in a firm s performance. For instance there is not so much data available on the patenting of start-up firms, since small firms report very little on their activities, there is no financial data regarding economic performance, before and after the patent was filled, published or granted, and there is no comparison data with a control group of nonpatenting start-ups. Furthermore, Helmers & Rogers (2011) state that since only a few patents protect really innovative and breakthrough inventions and some of these are associated with small firms, there is a parallel between the patent value distribution and the new firm performance distribution. Subsequently the authors refer the concept of the one in one hundred, from which is expected to value and bring to success one patent in one hundred. In terms of theoretical background there is a set of authors that have been working on the impact of the patent system on the performance of start-ups and innovation. 113

115 The effects of the geographic extension of patents, specific coverage of the international patent classification and the subsequent number of patent citations over the creation of new firms and subsequent growth were analyzed by Shane (2001), revealing the existence of a stimulus effect. Shane & Khurana (2003) analyzed the firm creation effect based on a patent licensed from the MIT (Massachusetts Institute of Technology), concluding that there is a sequential effect in the past entrepreneurial experience and the creation and growth of a start-up based on an invention. Jaffe & Lerner (2001) and Bessen & Meurer (2008) analyzed the possible inefficiencies of the patent system, addressing questions like patenting and minimizing competition, causing entry barriers to new firms, increasing of costs associated with sequential and incremental innovation and patent races. Other authors analyzed the trade-off between costs and benefits in choosing IP formal mechanisms versus informal mechanisms. For example Anton & Yao (2004) focused on the small and medium value innovations which are object of patenting instead of high value innovations. This is explained by the authors having in mind that if the property rights protection is weak, mainly in the cases of process inventions, the threat of imitation due to disclosure of an invention by patenting can be disadvantageous. Langinier (2004) focused on patents as a strategic barrier to entry. He concluded that if the demand from the market is high the patent can make the competitor stronger if he respects the novelty requirement, however if the demand is low and the patent holder renews the patent this will work against the firm. Nerkar & Shane (2007) reviewed the effect of attributes of inventions in their successful commercialization, being some inventions easier and less risky to transfer than others, for instance more applied inventions instead of more basic science based ones. The authors analyzed the impact of three attributes of technological inventions that influence the strategic performance of the commercialization and transfer process. Firstly, the scope of the patent which if it is broader can allow the appropriation of greater returns if commercialization is successful by covering a wider range of technical areas and also increases the likelihood of new firms being created to commercialize the invention. Secondly, the pioneering nature of the invention by increasing the incentive of owners to invest in the commercialization of the patent is able to provide the first mover effect and learning curve advantages, like the avoidance of imitators and the creation of similar products and processes. Thirdly, the age of the invention increases the possibilities of commercialization, since issues like uncertainty regarding the value of the patent and the inexistence of information on the market and the technology tend to disappear. Nevertheless the age attribute can also be a barrier, by decreasing the number of years of the patent, the returns from its commercialization decline and also by enabling more competitors to develop substitute products. Thus: 114 H 5 : The firm s growth has a positive and significant relationship with firms' license-out activity of internal patents. Kultti et al. (2007) also focused on motives of firms to opt for patents instead of other non-formal IP mechanisms such as secrecy. One such motive can derive from the fact that opting for a patent the firm can avoid the entrance of a possible competitor and be the first innovator in the market, assuring freedom to operate. This is the particular case of high-tech firms. Hall (2007) has devoted attention on the problematic about the decrease of the average quality of patents. Additionally there are some concerns regarding the role of patents in small firms and startups, since the high costs of patenting, the behavior of large firms, the fast growth in overall patenting and the uncertainties over enforceability are not in favor of that type of firms.

116 Hudson et al. (2007) refer to the comparison between patterns of patenting and generation and performance of start-ups in US which tends to be higher than in Europe. Mann & Sager (2007) analyzed the patenting behavior of venture-backed software start-ups in the US, denoting a positive impact of patents on firms performance, namely on the survival rate, growth and income received. Graham & Sichelman (2008) reviewed the role of patents to start-up firms underlying the possibility of bringing competitive advantages, since these firms will only be able of capitalizing over their knowledge and inventions if the later are protected by patents, excluding other firms from appropriating the outcomes of the assets. Additionally the referred authors confer patents the advantage of firms achieving a more secure protection, especially in inventions where imitation and reverse engineering are relatively easy. They also suggest the possibility of patents to works as a signaling mechanism for small and young firms securing venture investment and financing the transformation process of an intangible asset into a property right. The same authors call the attention for the possibility of start-ups to obtain income via licensing, being an attractive business model for start-ups that are not interested in producing and marketing their inventions. The motives that explain the investment of start-ups in building a patent portfolio are concerned with the possibility of blocking competitors, having bargaining power for cross-licensing agreements and assuring a defense mechanism when being accused of infringement over third parties patents rights. Following this line of reasoning, Hsu & Ziedonis (2008) pointed out that in order to obtain external finance, start-ups can affect in a positive way the investors valuation, by making use of patents as signaling mechanism for investors to preview the firm s potential. Accordingly, Colombo & Grilli (2010) studied the effects of the human capital of founders and their access to venture capital (VC) acting as key drivers of the growth and success of new technology-based firms. They conclude that for non-vc-backed firms the set of skills of founders is positively related to the growth of firms. Furthermore, for VC-backed firms their investors act as scouts conducting performance levels. In addition, Cucculelli & Ermini (2012), defend that the introduction of new products is positively correlated with growth in multiproduct firms, referring that new product is also associated with the growth of R&D intensive sector' firms and sectors that absorb externally originated patents. Thus: H 6 : The firm's growth has a positive and significant relationship with the firm s license-in activity of external patents. The appropriability regime and its strength can provide a barrier against imitation from competitors, creating sustainable advantages for the new firm entry and growth, either by limiting competition or by increasing the costs of competitors, or even increasing the value of the firm, providing additional bargaining power (Tuppura et al., 2010). This study is aligned with previous studies on appropriability as a key variable that influences the successful entry and growth strategies for radical innovations (Montaguti et al., 2002). Moreover, the authors stress that the higher the appropriability the higher the option for a penetration strategy, in the sense that this type of strategy requires protection from rapid competitive imitation. Parker et al. (2010) and Kosters (2010) focused their attention on high growth firms, the so called gazelles, and the role of patents in the growth performance of this type of firms. The concept of gazelle firms was firstly studied by Birch (1979). The author defined it as a small group of highgrowth firms responsible for the creation of the majority of new net jobs in economy. In contrast the Elephant firms correspond to the few large companies in charge of generating a large employment share, being however a few percentage of these jobs new. A third typology corresponds to the Mice firms, small, characterized by a very slowly growth and a reduced rate of employment growth. Joshi & Nerkar (2010) state that patents facilitate the markets for technology, since they reduce uncertainty giving the inventor the exclusive right to use for a specific period of time the knowledge 115

117 asset represented by the patent earning entrepreneurial incomes from licensing or exploiting the asset. Graham et al. (2010) focused on the use and usefulness of patents on start-ups. Firstly they differentiated between start-ups with and without venture capital. They also detected divergences among industries, for some sectors like biotechnology patents are of extreme importance, while for others, software for example, patents are avoidable. The authors concluded that patents provide limited incentives to invent and few advantages for commercializing innovations, because of the high costs around the system, being also difficult to avoid competitors to invent around. Aside of this, they recognize the importance of patents to avoid imitation and securing external sources of funding, by attracting reputation added to the intangible assets and thus supporting the firm in the growth path. 3. Methodology 3.1 The model Based on the literature review, a conceptual model is proposed, in which are explored the relationships between growth and determinant factors, namely, size and corporate R&D strategy factors (e.g. R&D intensity, patent portfolio and business model for patent management) as displayed in Figure 1. Fig. 1 Corporate R&D Strategy and Firm s Growth: Conceptual model Size R&D intensity Total patents License-out H 1 H 2 H 3 (R&D intensity) 2 - H 4 H 5 H Firm's growth License-in Dataset and model specification Variables and measurement The present paper makes use of the Kauffman Firm Survey (KFS) 1, which is a panel study of 4,928 firms founded in 2004 and tracked over their early years of operation until the next six years. This longitudinal panel was created from a random sample of the Dun & Bradstreet (D&B) s database list of new businesses started in 2004, embracing approximately two hundred fifty-thousand businesses. For achieving the goals of the present paper the KFS dataset was adapted in order to focus on the set of variables under analysis.

118 The present paper intends to estimate the effects of corporate R&D strategy factors on firm s growth, by using as proxies, the R&D intensity, the patent portfolio of the firm and the business model for patent management, which is characterized by license-in and license-out activities. We will control for activity, by making use of the NACE classification for high-tech and medium high-tech firms. For this purpose, we will focus on the manufacturing industries and service firms, especially the high-tech and medium high-tech firms. According to Coad & Rao (2008) it's important to avoid noise when selecting the proxies that are used to quantify innovativeness. To avoid the noise effect, we gather information on both innovative input (R&D efforts) and output (patents), assuring that we obtain useful data on the corporate R&D strategy, since we consider both R&D expenditure and patent data. The variables included in the conceptual model previously proposed are described in Table 1. The focus of the present paper resides on the assessment of the importance of a selected set of determinant factors related to the corporate R&D strategy on firm s growth, by using a sample of US start-ups. Some of the variables, for instance, R&D intensity and squared R&D intensity, were computed by making use of the variables R&D expenditures and total revenues. Furthermore, the firm s growth is computed through the average annual change of total assets, and the size s variable corresponds to the log of employees number. We will use as control variables the firm's technological intensity, based on the NACE classification from the OECD 2. Table 1. Measurements of the variables representing the conceptual model Variables Firm s growth Size R&D intensity (R&D intensity) 2 Total patents License-out License-in Technological intensity Measurement Average growth rate of period based on average annual change in firms' total assets. Log value of total number of employees. Mean R&D intensity per year, calculated by R&D expenditures over total revenues. Squared R&D intensity. Patent count. A dummy indicating whether the firm licensed out any patent. A dummy indicating whether the firm licensed in any patent. A control variable indicating the NACE classification of activity - in this case only the firms from NACE 32 and 33 and 72 corresponding to the high-tech sectors (OECD s definition of high-tech sectors for manufacturing firms in the cases of NACE 32 and 33 and knowledge intensive service firms in the case of sector 72). The NACE 31 corresponds to the set of medium high-tech sectors of manufacturing firms. In this paper the relationships between the firm s growth and the corporate R&D strategy factors, namely, patents owned by a firm, its R&D intensity, business model for patent management and size, were analyzed by using panel data analysis. The panel data has several advantages such as: (i) we can deal with more observations and less multicollinearity, which will increase precision in estimations; (ii) it gives the possibility of controlling for cross-section effects; and (iii) when extended to a dynamic model it gives the possibility of addressing potential endogeneity problems related to the explanatory variables. The population of the study consists of all firms (818) found on the KFS survey, from the high-tech and medium high-tech sectors, in the period In this survey we found three high-tech sectors, the 32 (Manufacture of radio, television and communication equipment and apparatus) and the 33 (Manufacture of medical, precision and optical instruments, watches and clocks) for manufacturing firms and the 72 (Computer and related activities) for knowledge intensive service firms and 1 medium high-tech sector, namely the 31 (manufacture of electric machinery and apparatus). 117

119 Selection of the model specification When considering the use of panel data the same cross-sectional unit is surveyed over a period of time, having as premise the fact that panel data has a space and time dimension. Due to the fact that we are dealing with a panel data of firms, or other units like for instance individuals or states, over time, it's possible to observe heterogeneity in these units. Additionally, by the combination of timeseries of cross-section observations, the panel gives more informative data, more variability, less collinearity among variables and increased efficiency. The analysis hereafter presented is based on pooled ordinary least squares (OLS), and both random and fixed effects panel estimations. Greene (2008) presents the basic regression model as follows: Y it X it z i it where i = 1,2,...N, referring to a cross-section unit, t = 1,2,...T, relating to time period, Y it corresponds to the dependent variable, being X it the explanatory variables, without the inclusion of a constant term, ε it refers to the disturbance term and β are the unknown coefficients which vary in relation to individuals and time. The individual effect is given by z i α where z i contains a constant term and additionally a set of individual or group specific variables. In the case where z i is unobserved and correlated with X it the least squares estimator of β is considered biased and not consistent due to an omitted variable. The model is expressed in the following terms: (1) Y it X it i it (2) considering α i = z i α contains all the observable effects and specifies an estimable conditional mean. In this sense, this fixed effects perspective assumes α i as a group-specific constant term in the regression model. In the case of unobserved individual heterogeneity, although formulated, it can be assumed to be uncorrelated with the included variables, being the model then formulated as follows: Y it X it u i it (3) being this random effects perspective what specifies that u i is a group specific random element. By considering the static panel data models and regarding the determinants of firm s growth for the present study the estimation can be presented through the following models: Firm s growth ' 2 1 Total patents R & D Intensity (4) Model I: it it it i it 118 Firm ' s growth Model II: it 1 Total patents it 2 R & D Intensity it License in 4 License out i it 3 it it (5)

120 Firm ' s growth Model III: it 1 Total patents it 2 Size it 3 R & D Intensity it 2 R D Intensity it 5 License in License out i it 4 & it 6 it (6) 4. Results and discussion The choice of the better model was based on the assumption of the Hausman Test. This test implies the presence of a significant correlation between individual specific effects and the set of explanatory variables. According to Greene (2008) when performing the Hausman Taylor test, in order to decide between fixed or random effects, and being the null hypothesis that the preferred model is random versus the alternative fixed effects as it tests if the unique errors (u i ) are correlated with the regressors stating the null hypothesis that they are not, we can conclude for choosing the fixed effects model, since the P-value is (i.e. statistically significant) which is lower than In Table 4 (Model III) one can see the results of all explanatory variables on firm s growth. The fixed effects model was chosen as the best model, since the Hausman Test result obtained the value of for a P-value of The results from the estimation of the static panel models are presented in tables 2, 3 and 4 below. Table 2. Static panel models (Model I) Dependent variable: Firm growth Independent variables: Total patents R&D intensity Observations Wald F R 2 Hausman (χ 2 ) Random effects ( ) -6.90e-08 (5.33e-06) *** Fixed effects ( ) (6.60e-06)*** Notes: Robust standard errors are presented within brackets. The Wald tests are used for testing the null hypothesis of non-common significance of the parameters of the explanatory variables against the alternative hypothesis of common significance of the parameters of the explanatory variables. F tests the null hypothesis of non-common significance of the estimated parameters against the alternative hypothesis of common significance of the estimated parameters. *significant at 10% **significant at 5% ***significant at 1% As illustrated in table 2, the results of the F and Wald tests show that there are some effects of the explanatory variables on the dependent variable. Although the total number of patents has no significant effect on firm s growth, R&D intensity on its side has a negative and significant (at 1%) effect on the dependent variable. Thus, we fail to reject hypothesis H 2, denoting a negative although significant impact on the dependent variable. By analyzing the results obtained for the two methods, we can state that by not considering the existence of individual effects, the impact of some variables on the dependent variable, in this case the R&D intensity, is under-valuated, in the sense that the coefficient of the variable increases considerably when the fixed effects model is performed. 119

121 The result obtained through the Hausman test, allows us to reject the null hypothesis at 1% significance level. Moreover, it points out that the non-observable individual effects are not correlated with the explanatory variables. Thus we can conclude that the most adequate method to perform the estimation is the fixed effects method. Next table show the results of the estimation for the model II, adding to model I the patents transactions, either by the license-in of patents or by the license-out of patents. Table 3. Static panel models (Model II) Dependent variable: Firm growth Independent variables: Total patents R&D intensity License-in License-out Observations Wald F R 2 Hausman (χ 2 ) Random effects ( ) -2.30e-06 (5.41e-06) *** ( ) ( ) ** Fixed effects ( ) *** (6.68e-06) *** ( ) ( ) *** Notes: Robust standard errors are presented within brackets. The Wald test is used for testing the null hypothesis of non-common significance of the parameters of the explanatory variables against the alternative hypothesis of common significance of the parameters of the explanatory variables. F tests the null hypothesis of non-common significance of the estimated parameters against the alternative hypothesis of common significance of the estimated parameters. *significant at 10% **significant at 5% ***significant at 1% 120 The results of the F and Wald tests also reveal some impact of the explanatory variables on firm s growth. Despite the fact that total number of patents and the license-out variables show no significant effect on firm s growth, license-in denotes a positive and significant impact (at 1%) when considering the random effects model. Running the fixed effects model we can state that besides the positive and significant impact of license-in over firm s growth, R&D intensity also denotes a negative and significant (at 1%) effect on the dependent variable. Thus we fail to reject hypothesis H 2 concerning the existence of a significant effect of R&D intensity on firm s growth, although in a negative way and we also fail to reject hypothesis H 6, stating that there exists a positive and significant impact of patents license-in over the firm s growth. The Hausman test result show us that by rejecting the null hypothesis at 1% significance level we conclude that the fixed effects method is the most adequate method to perform the estimation. Table 4 presents the results of the estimation for the model III, where we add to model I and model II the firm size and the squared R&D intensity. Table 4. Static panel models (Model III) Dependent variable: Firm growth Independent variables: Total patents Random effects Fixed effects

122 R&D intensity License-in License-out Size (R&D intensity) 2 Observations Wald F R 2 Hausman (χ 2 ) ( ) 5.88e-06 ( ) *** ( ) ( ) ( ) -9.69e-12 (2.20e-11) *** ** ( ) *** ( ) *** ( ) ( ) ( ) -1.28e-10*** (2.70e-11) *** 18.43*** Notes: Robust standard errors are presented within brackets. The Wald test is used for testing the null hypothesis of non-common significance of the parameters of the explanatory variables against the alternative hypothesis of common significance of the parameters of the explanatory variables. F tests the null hypothesis of non-common significance of the estimated parameters against the alternative hypothesis of common significance of the estimated parameters. *significant at 10% **significant at 5% ***significant at 1% The results obtained for the F and Wald tests denote significant impact of the set of explanatory variables on the dependent variable. When using the random effects method, the results of the estimations point to a positive and significant impact (at 1%) of license-in on the firm s growth, although we verify the inexistence of significant effects concerning other explanatory variables. When contrasting the results obtained through the two methods, we can defend that when we don t consider the existence of individual effects, the impact of some variables on the dependent variable is under-valuated. In this sense the coefficients of some variables increase considerably when we run the fixed effects model. The total patents, license-out and size denote no significant impact on firm s growth. Nevertheless the license-in of patents and the R&D intensity show a positive and significant effect (at 1%) over the firm s growth. Furthermore, the squared R&D intensity denotes a significant effect (at 1% level) on the firm s growth although in a negative way. In this way we fail to reject hypothesis H 2 and H 6 concerning the existence of a strong effect of R&D intensity and license-in of patents over the firm s growth, and also fail to reject hypothesis H 6, which argues there exists a significant impact of the squared R&D intensity on the firm s growth, since we find empirical evidence about the existence of an U-inverted shaped curve, concerning the relationship between growth and R&D intensity. The Hausman test result show us that by rejecting the null hypothesis at 1% significance we conclude that the fixed effects method is the most adequate method to perform the estimation. In the present paper, firms are displayed in two groups by NACE classification corresponding to the firms belonging to the high-tech sector namely, NACE 32 (manufacturing firms of radio, television and communication equipment and apparatus), NACE 33 (manufacturing firms of medical, precision and optical instruments, watches and clocks) and NACE 72 (knowledge intensive service firms in the 121

123 field of computer and related activities) and firms belonging to the medium high-tech sector namely, the NACE 31 (Manufacture of electric machinery and apparatus). In this sense the previous model was expanded with group-specific effects for the NACE classification, for providing tests for the sub-groups of firms. Coefficients of the explanatory variables related to each of the four groups were obtained from the model III which can be defined as follows. Firm' s growth it Group b b b 1 b 1 c 1 bc Explanatory Variables cit it In table 5 the effects of the set of explanatory variables on each of the four groups of medium hightech firms and high-tech firms, are presented. Table 5. Effects of explanatory variables on firm s growth by NACE classification: Static panel model (Model III) Dependent variable: Fixed effects Firm s growth Independent variables: Total patents Medium high- tech High-tech firms firms NACE 31 NACE 32 NACE 33 NACE ( ) ( ) ( ) ( ) (7) R&D intensity License-in License-out Size (R&D intensity) 2 Observations F R e-06 ( ) ( ) ( ) ( ) 1.23e-11 (1.32e-10) ** ( ) *** ( ) ( ) ( ) -4.35e-10*** (1.05e-10) e-06 ( ) ( ) ( ) ( ) -1.02e-11 (1.99e-11) e-06 ( ) ( ) ( ) ( ) 4.45e-10 (2.69e-08) Notes: Robust standard errors are presented within brackets. Wald tests the null hypothesis of noncommon significance of the parameters of the explanatory variables against the alternative hypothesis of common significance of the parameters of the explanatory variables. F tests the null hypothesis of non-common significance of the estimated parameters against the alternative hypothesis of common significance of the estimated parameters. *significant at 10% **significant at 5% ***significant at 1% 122 Regarding the effects of the set of explanatory variables on firm s growth by firm sector and considering that in our sample of high-tech and medium high-tech sectors we have founded 4 sectors, namely the 31 (manufacture of electric machinery and apparatus) corresponding to the medium high- tech sector of the sample, the 32 (manufacture of radio, television and communication equipment and apparatus), the 33 (manufacture of medical, precision and optical instruments, watches and clocks) and the 72 (computer and related activities) corresponding to the high-tech sector of the sample, we can conclude that for the sectors 31, 33 and 72 the explanatory variables show no impact on the explained variable. On the contrary, and confirming the results obtained

124 through the F test, which denote impact of the set of explanatory variables on the dependent variable, for the sector of manufacture of radio, television and communication equipment and apparatus, which is an high-tech manufacturing sector, the license-in of patents denote a positive and significant effect (at 1% level) on firm s growth. Additionally, the R&D intensity shows a positive and significant impact on the firm s growth (at 5% level) and the squared R&D intensity denotes a negative and significant impact on the firm s growth (at 1% level). We therefore support previous considerations by failing to reject hypothesis H 2, H 3 and H 6, being a positive effect for H 2 and H 6 and a negative effect for H 3. With the intention of verifying if the firm s growth is adjusted by the effect of the set of explanatory variables under analysis and in order to contrast the results obtained through the static panel estimation we will present the results of the dynamic panel coefficients. By considering the determinants of firm s growth, previously defined, the estimation can be presented in accordance with: Model I: Firm' s growth it Firm ' s growth it Total patents it 2 R & D Intensity it i it (8) Model II: Total patents R & D Intensity Firm ' s growth it Firm ' s growth it it 2 it 3 License in it 4 License out it i it (9) Model III: Firm Total patents R & D Intensity ' s growth it 2 Firm ' s growth it it it 2 R D Intensity it 4 License in 5 License out Size i it 3 & it it 6 Next, we present the results of the dynamic estimator GMM (Generalized Method of Moments) for the three models under consideration. Results are presented in table 6 below. it (10) Table 6. Dynamic model GMM for explanatory variables on firm s growth Dependent variable: Firm growth Independent variables: Model I Model II Model III Firm growth it ( ) ( ) ( ) Total number of patents ( ) ( ) ( ) R&D intensity *** ( ) *** ( ) *** ( ) License-in *** ( ) *** ( ) License-out ( ) ( ) 123 Size ( )

125 (R&D intensity) e-10*** (3.99e-11) Instruments Observations Wald GMM *** GMM *** GMM ** Notes: Robust standard errors are presented within brackets. Wald tests the null hypothesis of noncommon significance of the parameters of the explanatory variables against the alternative hypothesis of common significance of the parameters of the explanatory variables. *significant at 10% **significant at 5% ***significant at 1% 124 Taking into account the results obtained through the F test, for the first two models, at a 1% significance level, and for the third model, at a 5% significance level, we can conclude that the explanatory variables are determinant to firm s growth. The parameter that measures the impact of last period firm s growth on the present period firm s growth is not statistically significant in none of the three models. Moreover, when applying the dynamic model we can confirm that there are no significant changes to the results achieved through the static panel estimations, being this effect similar to the application of the three models under consideration. Regarding the model I the variable R&D intensity has a negative and significant effect on the firm s growth. Therefore we fail to reject hypothesis H 2. In what concerns the model II the introduction of additional variables like license-in and license-out doesn t change the overall statistical significance of the estimation when compared with the static model, as the explanatory variable R&D intensity maintains its negative and significant effect and the license-in of patents still denotes a positive and significant effect on the firm s growth. We also fail to reject hypothesis H 2 and H 6. When we add firm s size and the squared R&D intensity to the model III the positive and significant effect of the independent variable R&D intensity on the dependent variable is ratified, as it happens with negative and significant effect of the squared R&D intensity. In this model we also find a positive and significant effect of the license-in of patents in explaining firm s growth. We still fail to reject hypothesis H 2, H 3 and H 6. To go a little bit deeper in explaining the effects of the set of independent variables on explaining firm s growth we expanded the model III, since it is the most complete, with the group-specific effects for the NACE classification, having tested it for the sub-groups of firms. In table 7 the coefficients of the explanatory variables related to each of the four groups are displayed. Table 7. Effects of explanatory variables on firm growth by NACE classification: Dynamic panel model (Model III) Dependent variable: Firm growth Independent Medium high- tech High-tech firms variables: firms NACE 31 NACE 32 NACE 33 NACE 72 Firm Growth it *** ( ) ( ) ( ) ( ) Total patents R&D intensity License-in ( ) ( ) ( ) *** ( ) ( ) ( ) ( ) ( ) ***

126 License-out ( ) ( ) ( ) ( ) Size ( ) ( ) ( ) - (R&D intensity) ( ) *** ( ) ( ) ( ) Observations Wald 4.61e-11 (2.08e-10) -6.21e-10*** (1.62e-10) -1.36e-11 (2.70e-11) 7.69e-09 (2.95e-08) *** *** Notes: Robust standard errors are presented within brackets. The Wald test is used for testing the null hypothesis of non-common significance of the parameters of the explanatory variables against the alternative hypothesis of common significance of the parameters of the explanatory variables. F tests the null hypothesis of non-common significance of the estimated parameters against the alternative hypothesis of common significance of the estimated parameters. *significant at 10% **significant at 5% ***significant at 1% Regarding the effects of the set of explanatory variables on firm growth by firm sector and considering the sectors 31, 32, 33 and 72, we can conclude that for the sectors 31 (medium high-tech firms) and 33 (high-tech firms) the explanatory variables show no significant effect on the dependent variable. This result is similar to the one that was obtained through the estimation of the static model. This finding is ratified through the Wald test results, which denote a significant impact of the set of explanatory variables on the explained variable, for the sector of manufacture of radio, television and communication equipment and apparatus (NACE 32, corresponding to high-tech firms) the license-in of patents denotes a positive and significant effect (at 1% level) on firm s growth, as well as the R&D intensity that shows a positive and significant impact on the firm s growth (at 5% level for the static model and 1% for the dynamic model) and the squared R&D intensity that denotes a negative and significant impact on the firm s growth (at 1% level). The only effect that is different from the static model is the one that is relative to the impact of size on firm s growth, which in the case of the dynamic model is positive and significant. Furthermore, for the sector of computer and related activities (NACE 72 which corresponds to an high-tech knowledge intensive service firms' sector), when performing the dynamic model the lagged variable of firm s growth shows a negative and significant impact (at 1% level) on the explained variable, denoting that the firm s growth on the present moment is impacted negatively by the firm s growth on the previous moment for the computer and related activities firms. 5. Concluding remarks This study uses the concept of firm s growth in seeking to uncover the effects of a set of explanatory variables on its dynamics. Firm s growth, as a topic of research that has been target of several studies, was hereby analyzed under a different lens, e.g., by assessing the effects of corporate R&D strategy factors (such as R&D intensity, patent portfolio and business model for patent management) on start-ups growth. The dimension and richness of the US start-ups dataset used allows us to achieve an unusual observation of the evolution over time of 4 different NACE sectors, namely the high-tech and medium high-tech sectors in a sample of 818 firms extracted from the KFS survey that contains 4928 firms, in a 6 year period. 125

127 126 While previous studies also focused on the role played by innovation proxies, such as the R&D intensity, or the patent portfolios on firm s growth, the present paper went a little bit further obtaining in innovative way information about corporate R&D strategies based on business models for patent management, the license-in and license-out of patents in firms, and its impact on start-ups growth. In addition, the present study contributes to existent literature by gathering more information on the effects of innovation proxies on firm s growth expanding the analysis to understand this effect on high-tech and medium high-tech sectors, where the pace of technological change is usually high and tends to shorten life cycle products, and where firms tend to rely on their IP rights and on the effect early-mover (Tuppura et al., 2010). Our results indicate that the set of explanatory variables representing the corporate R&D strategy determines the start-ups growth. Comparing the results of the three models in static and dynamic panels, we confirm that there are no major or significant changes in the results achieved. Overall, R&D intensity appears to play a high and positive impact on start-ups growth, in both static and dynamic estimations. The same effect is detected on the positive and significant impact of the license-in of patents on start-ups growth. When we add the squared R&D intensity we verified the existence of a U-inverted relationship between start-ups growth and R&D intensity. Our results confirm that only for the sector of manufacture of radio, television and communication equipment and apparatus (NACE 32, which corresponds to a high-tech manufacturing sector) the activity of license-in of external patents has a positive and significant effect on firm s growth and the R&D intensity denotes a negative and significant impact on start-ups growth. In addition for the dynamic estimation the effect of firm s size on start-ups growth reveals to be positive and significant. Furthermore, the empirical evidence now obtained reveal that for the sector of computer and related activities (NACE 72, which corresponds to an high-tech knowledge intensive service sector), the lagged variable of firm s growth undertakes a negative and significant impact on firm s growth, detecting a negative correlation of the firm s growth at the present moment with the firm s growth at a previous moment. In general, our study reveals the mechanisms for patent transaction that can influence the firm s growth, especially when considering small and young start-ups, with an average size of 2.2 employees, created in 2004 and traced for the next 6 years. In this framework, the patent portfolio doesn t affect the start-ups growth, neither the firm size denotes a major impact on their performance, excluding the later effect on start-ups belonging to the sector 32 which relates to a high-tech manufacturing sector. The license-in of patents reveals to be significant and positive for the growth path of the firm and also its R&D intensity with particular focus on the high-tech manufacturing sector. Future avenues for research into the role played by the patent portfolios, innovative business models for patent management (e.g. coopetitive licensing) and R&D intensity should include the examination of different sectors, especially the innovative service firms in order to assess the possibility of shedding more light to the variations of growth, R&D and intangibles across industries and business activities. Consequently, different firms should use different strategies and business models for improving performance and subsequently leveraging growth. For instance, for small young firms the technology transfer activities and the open innovation schemes related to the management of patents, especially by licensing external IP rights, can be of extreme importance to foster growth patterns. Of particular interest is the analysis of the relationship between growth and R&D intensity, which is characterized by a U-inverted relationship, being positive and significant at an initial stage but revealing to be negative later on, when dealing with this type of start-up firms, especially high-tech and medium high-tech firms.

128 In terms of implications for policy makers and entrepreneurs, a problematic for future debate is: the spillover effect of open innovation and coopetition strategies, in helping young firms to consolidate their growth path. Although having a patent portfolio at an initial phase doesn t impact on the startup s growth, it is an important factor for strengthening the corporate R&D strategy. This point is also ratified by the positive and significant effect of license-in of external patents which proved to be determinant to the start-ups growth. End notes: Acknowledgement: Selected data are derived from the Kauffman Firm Survey release 6.0. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Ewing Marion Kauffman Foundation. Sectors are designated as high-tech or low-tech following the standard OECD sector classification based on NACE Rev.2 at 3-digit level to compile aggregates related to high/medium technology and low-technology (http://epp.eurostat.ec.europa.eu/cache/ity_sdds/annexes/htec_esms_an3.pdf, accessed in: 2012/03/05). References Anderson, B. and Eshima, Y. (2011). The influence of firm age and intangible resources on the relationship between entrepreneurial orientation and firm growth among Japanese SMEs. Journal of Business Venturing, Available online 12 November 2011, ISSN , /j.jbusvent (http://www.sciencedirect.com/science/article/pii/s ). Anton, J. and Yao, D. (2004). Little patents and big secrets: managing intellectual property. RAND Journal of Economics, 35 (1), Ardishvili, A., Cardozo, S., Harmon, S. and Vadakath, S. (1998). Towards a theory of new venture growth. Babson Entrepreneurship Research Conference, Ghent, Belgium. Audretsch, D., Klomp, L., Santarelli, E. and Thurik, A. (2004). Gibrat s Law: Are the Services Different?. Review of Industrial Organization, 24(3), Barney, J. (1991). Firms resources and sustained competitive advantage. Journal of Management, 17, Baumol, W. (1990). Entrepreneurship: productive, unproductive, and destructive. Journal of Political Economy, University of Chicago Press, 98 (5), Bessen, J. and Meurer, M. (2008). Patent Failure. Princeton University Press. Princeton. Bharadwaj, A., Baradwaj, S. and Konsynski, B. (1999). Information technology effects on firm performance as measured by Tobin's q. Management Science, 45 (June), Birch, D. (1979), The Job Generation Process, unpublished report prepared by the MIT Program on Neighborhood and Regional Change for the Economic Development Administration, U.S. Department of Commerce, Washington, DC. Botazzi, G., Dosi, G., Lippi, M., Pammolli, F. and Riccaboni, M. (2001). Innovation and corporate growth in the evolution of the drug industry. International Journal of Industrial Organization, 19, Canibano, L., Garcia-Ayuso, M. and Sanchez, P. (2000). Accounting for intangibles: a literature review. Journal of Accounting Literature, 19, Coad, A. and Rao, R. (2008). Innovation and firm growth in high-tech sectors: A quantile regression approach. Research Policy, Vol. 37(4), May, Colombo, M. and Grilli, L. (2010). On growth drivers of high-tech start-ups: Exploring the role of founders' human capital and venture capital. Journal of Business Venturing, Vol. 25(6), November, Cucculelli, M. and Ermini, B. (2012). New product introduction and product tenure: What effects on firm growth? Research Policy, Vol. 41(5), June, Cuervo, A. (2005). Individual and environmental determinants of entrepreneurship. International Entrepreneurship and Management Journal, 1,

131 THE CLUSTERING OF CORK FIRMS IN SANTA MARIA DA FEIRA: WHY HISTORY MATTERS João Carlos Lopes 1, Amélia Branco 2 1 ISEG - School of Economics and Management, Technical University of Lisbon, and UECE - Research Unit on Complexity and Economic, Lisbon, Portugal. 2 ISEG - School of Economics and Management, Technical University of Lisbon, and GHES Group of Social and Economic History, Lisbon, Portugal. Abstract. This papers studies the reasons why most of the Portuguese cork manufacturing firms are concentrated in Santa Maria da Feira, a small county in the North of the country, whereas the bulk of the cork is produced in the South (Alentejo and Ribatejo). It starts with a brief introductory discussion of the advantages and limitations of clusters and industrial districts along the recent theoretical findings of evolutionary economic geography. Next, a comparative analysis of the economic performance in the last decade, of firms localized in Santa Maria da Feira and other regions is made, from which a strong conclusion emerges that points to an absence of clear advantages of clustered firms. Then, an exercise is made of searching the fundamental roots of the Feira s cluster in historical and socio-political reasons that are shown to be indispensable to understand and substantiate the business localization decisions of most of the cork Portuguese entrepreneurs. Keywords: Cork industry; clusters; Portugal Introduction The cork industry is an important economic activity in Portugal, a country which is by far the largest world producer and exporter of manufactured cork products, responsible for 62 percent of the 804,7 million Euros in value exported worldwide. Corks for wine bottles are the leading product, representing about 70% of total exports (563 million Euros, of which 352 million are exports of natural corks). Cork products contribute to more than 2% of total Portuguese exports and around 30% of exports of forest products. The main export destinations are the wine-producing countries, notably France, Italy and the U.S.A. (APCOR 2011). The nearly 600 companies belonging to this sector employ more than workers and produce about 40 million corks per day, of which 35 million are produced in Santa Maria da Feira, a small county in the North of the country, belonging to the district of Aveiro. The main purpose of this paper is to study the reasons why most of the Portuguese cork manufacturing firms are concentrated in this northern district, whereas the bulk of the raw material (natural cork) is produced in the southern regions of Alentejo and Ribatejo. It starts with a brief analysis of the advantages and limitations of clusters and industrial districts, along the recent theoretical findings of evolutionary economic geography, as well as the older perspectives of Porter and Becattini, largely based in Marshall (section 2). In section 3, a quantitative assessment is made of the relative economic performance of firms clustered in Santa Maria da Feira and firms dispersed in other regions of the country, using the most recent data available from Portuguese national statistics institute (INE) about this industry: production, employees, hours worked, labour productivity and international trade. This exercise covers the period , and it seems to uncover an absence of clear advantages of clustered firms. With this result in mind, the main part of the paper (section 4) is dedicated to a careful effort of searching and describing the fundamental roots of the Feira cork cluster in historical and sociopolitical reasons that are shown to be indispensable to understand and substantiate the business localization decisions of most of the cork Portuguese entrepreneurs.

132 Finally, section 5 ends the paper with the main concluding remarks and suggestions of future lines of investigation, for a better understanding and improving of the functioning of this important sector for the Portuguese re-industrializing effort in course, essential to overcome the serious macroeconomic and financial crises of this country. 2. The importance of Clusters and industrial districts In the 1990s, the approach of Alfred Marshall to external economies in his book Principles of Economics (1890) was renewed by Michael Porter (Porter 1990, 1998) with the introduction of the cluster concept. In the definition of this new concept we can find many features of the Italian Industrial Districts (Becattini, 1990) or the geographical agglomerations of Marshall. Clusters can be understood as geographical concentrations of interconnected companies, specialized suppliers, service providers and institutions, competing and cooperating in a same space, at a national or a regional level (for a detailed analysis of this concept see Martin and Sunley, 2003). The clustered companies are connected to other companies and institutions also inside the cluster, through exchange relations and mutual interdependencies. Clusters are important for economic development since the companies inside the cluster experience a stronger growth, resulting from competitive advantages determined by the interaction of the four points of the competitive diamond (Porter 1991): factor (input) conditions; context for strategy and rivalry; demand conditions and related and supporting industries; all of them influenced by other factors (for instance, chance or Government policy). The geographical agglomeration of firms increase the potentialities of the diamond, reinforced by the local economic and social history that strength the links between companies and institutions locally set. The research agenda about clusters has been marked by the analysis of its functioning and not so much on its origin, development or even decline. The importance of a more profound analysis of these aspects of the cluster may shed some light to understand the factors that underpinned the emergence of a cluster. The evolutionary economic geography developed a new approach of the clusters, considering its life cycle (Boschma and Frenken 2006; Martin and Sunley 2011), studying the evolution of the cluster since its origin, during its development and maturity or even decline. In this context, the historical approach is very useful since the past (or historical) choices in terms of productive specialization, technologies, labour skills, network of suppliers, etc., can create a path and past dependency and ultimately lead to lock-in situations. According to Martin and Sunley (2006), the possible sources of regional path dependence are: natural resource based; sunk cost (facilities, machinery, etc.); local external economies of industrial specialization; regional technological lock-in; economies of agglomeration; local institutions and socio-cultural features; regional dependencies in relation to other region or political decisions in other regions. The regional dependence explains the path dependence: the same factors responsible for the development of one or few firms may cause de creation of other firms in the same local. Moreover, some random and historical accidents or events may divert the cluster from the first path dependency, conducting it to a self-reinforcing mechanism that could be either positive or negative. But the results are still not predictable. The cluster presents endogenous and exogenous factors that shape the local productive system and it can learn during its evolutionary process, pressed by the competition or by exogenous (international or national) factors. As Belussi and Sedita (2009: 508) state, the cluster is an evolving complex system and it exhibits some learning capacity. Considering the approach of the "life cycle", initially the cluster experiences a rapid expansion and accumulation of capital resources in terms of expertise, knowledge and support institutions. In a second phase it tends to stabilize in terms of structure and shape. The degree of interconnection is high and this can make the cluster less resilient. However, the cluster may become mature, 131

133 132 depending on its flexibility and type of external shocks to which it will be exposed. Facing a competitive shock, the cluster may disappear or diminish in size. However, the transition to a phase of disappearance is not so linear. Resilience can be understood as the adaptation capability of a system. In the case of the cluster, two trends are in conflict: on the one hand, clusters increase their internal interconnections; on the other hand, the growing interconnections reduce the adaptive capacity of the system with respect to external shocks. This means that there is a trade-off between resilience and interconnections: the more interconnected are the parts of the system more rigid it will be in structural and functional terms. The adaptive life cycle model seeks to reconcile these two tendencies, albeit with unpredictable results in terms of success and survival. Martin and Sunley (2006) consider six options regarding the evolution of a mature cluster: 1) emergence of a new cluster that leverages the resources and capabilities inherent to the former; 2) constant mutation cluster and in this case, it fits and is constantly evolving, for both new sectors as for new activities (high degree of resilience); 3) stabilization cluster for a long period, for example, by taking advantage of market niches, but always with the threat of disappearance (modest degree of resilience); 4) reorientation of the cluster, corresponding to the emergence of a new cluster; 5) emerging cluster failure for not achieving the critical mass to exploit external economies; 6) disappearance of the cluster, according to classic life cycle theory. Also according to Martin and Sunley (2006), the triggering factors could permit the end of the lock-in situation, i.e., the rigidity and inflexibility of the cluster when confronted to external challenges. Likewise, Belussi and Sedita (2009) highlight the qualitative aspects of the cluster in the several phases of its life cycle. The existence of some local endowments (input conditions, for instance, qualified or specialized workers; natural resources, etc.), institutions and anchor firms, are some of the endogenous factors explaining the genesis of the cluster. In the development and maturity phases, several other endogenous factors can be important, like: technological innovation; universities, research centers, business networks; aggressive strategies like diversification of products and markets. Among the exogenous factors, the growth of demand, the internationalization and globalization processes for instance, when a local company becomes a multinational company - are determinant, representing simultaneously a challenge and a menace to the cluster. These factors put the cluster in contact with outside customers, suppliers, institutions, competitors that may transform the cluster in an open and global system. Menzel and Fornahl (2009) conclude that the cluster can be also distinguished by a quantitative dimension, considering the number of firms and employees. In the origin, the number of firms is small but is growing, mostly small firms. In the development phase the number of employees growths significantly, comparing to the national situation. In the mature phase the cluster is able to sustain the level of employment. The declining phase is marked by the diminishing of the number of firms and employees. Considering and explaining the factors that are in the origin of path dependency of the cluster and its hypothetical lock-in, certainly represents a considerable contribution for possible solutions to put an end to the lock-in situation. Adopting an historical approach we intend to identify the factors that are in the genesis of the Cluster of Feira and if during its life cycle it has developed a delocking mechanism. Starting the empirical analysis with the picture of the cluster in the last decade, of firms localized in Santa Maria da Feira and other regions, certainly disclose the significance of empirical analysis based in the historical knowledge of the cluster of Feira.

134 3. The economic performance of Feira s firms and other firms compared According to the theory about clusters and industrial districts briefly described above, it would be expectable that firms belonging to a strong and resistant cluster show economic and financial advantages over the firms of the same sector localized in other regions of the country. It is an interesting exercise to compare the performance of these two groups of firms and see if the above expectation is confirmed. In order to do so, a diversified number of indicators are used, based on data from the Portuguese official statistics institution (INE) and the period covered is It is a difficult phase for the Portuguese economy as a whole, and for manufacturing sectors in particular, marked by two serious recessions in 2003 and Of course, the cork sector in Portugal could avoid these global difficulties, as can be seen in Table 1 that shows its main indicators in the beginning and end years of the period, namely, the absolute and relative numbers, as well as the rate of change, of firms, employees, production, value added and investment, in the area of Santa Maria da Feira (around 80 per cent of the sector s firms) and in other regions of Portugal (mostly Setúbal and Algarve). Table 1. Main indicators of the Cork Industry in Portugal, Firms Year S. M. Feira Other regions Portugal Number % of Tot. R. Ch. (%) Number % of Tot. R. Ch. (%) Number R. Ch. (%) , , ,2-26, ,8-28, ,9 Year Employees S. M. Feira Other regions Portugal Number % of Tot. R. Ch. (%) Number % of Tot. R. Ch. (%) Number R. Ch. (%) , , ,5-33, ,5-14, ,6 Year Production S. M. Feira Other regions Portugal Million % of Tot. R. Ch. (%) Million % of Tot. R. Ch. (%) Million R. Ch. (%) ,8 75,8-330,5 24, , ,5 84,9-34,5 353,2 15,1 6, ,7-25,3 Year Value Added S. M. Feira Other regions Portugal Million % of Tot. R. Ch. (%) Million % of Tot. R. Ch. (%) Million R. Ch. (%) ,4 83,8-51,4 16,2-316, ,9 83,6-11,5 45,9 16,4-10,7 280,8-11,4 Year Investment (Gross Fixed Capital) S. M. Feira Other regions Portugal Million % of Tot. R. Ch. (%) Million % of Tot. R. Ch. (%) Million R. Ch. (%) ,6 66,3-13,0 33,7-38, ,0 37,6-64,9 14,9 62,4 14,6 23,9-38,1 Source: INE and authors calculations As we can see, the main trend between 2004 and 2010 is the significant decay of the Portuguese cork sector, almost 30% in firms and employees, 25% in production, 11,4% in value added and an astonishing value of 38% in investment. However, some regional nuances are worth mention, namely that although diminishing less in the number of firms, the employment decay in Santa Maria da Feira was more than double relative to other regions, such that, in 2010, to 80% of the firms corresponds only 67,5% of employment. And contrary to the expectations from the evolutionary economic geography analysis about the advantages of clusters and industrial districts, the production of Feira s firms diminished 34,5% in this period, comparing to a surprising growth of 7% in the production outside this region. This situation is parallel to what has happened to a crucial variable to the competitiveness and sustained growth of any industry, investment in gross fixed capital. In this case, we saw a worrying decay of 64% in the 133

135 Feira and an appreciable growth of 15% in other firms. Considering this best performance of the firms out of Feira, it is however somehow puzzling the huge and stable weight of value added generated in Feira s firms, more than 83%. So, in order to better assess the relative performance of clustered (in Santa Maria da Feira) and no clustered firms (dispersed in other regions of Portugal), it is necessary to look at other indicators, namely the evolution of productivity and exports in the period under analysis. The best indicator of labour productivity is the value added generated by hour worked, but, as the series of INE do not include hours of work in the cork industry by region, we will use value added by worker (Table 2). Table 2. Productivity in the Cork industry, Year S.M. Feira Other Regions Portugal Prod R.Ch. (%) Prod R.Ch. (%) Prod R.Ch. (%) ,6 n.a ,9 n.a ,9 n.a ,2 6, ,3-5, ,4 4, ,9-1, ,2 24, ,1 2, ,8 4, ,3 35, ,8 9, ,1-16, ,8-16, ,5-14, ,4 0, ,1-39, ,2-13, ,0 45, ,3 29, ,1 41, ,93-0,80-3,66 Source: INE and authors calculations Labour productivity tends to be a strong pro-cyclical indicator, with large short run variability. In fact, looking at the sector in Portugal, some years show a large increase, namely 2010 and others a significant decrease, for instance 2008 and But it is also interesting to observe the large regional differences in this indicator. The firms outside the region of Maria da Feira had a good increment of productivity in 2006 and 2007, but the recession of 2009 affected them tremendously, with a fall of almost 40%. Although the annual average rate of growth in this period is clearly grater in Feira s firms, this is mainly because they had been particularly resilient to this serious macroeconomic crises and its aftermath. Another important indicator of the strength, competitiveness and sustainability of an industry is the evolution of its exports. Fortunately, INE published detailed export data by region, covering already the year of 2011, both in quantity (Kg of cork - Table 3) as in value terms (millions of - Table 4). 134 Table 3. Exports of the Cork industry, quantities Year S.M. Feira Other Regions Portugal Kg R.Ch. (%) Kg R.Ch. (%) Kg R.Ch. (%) n.a n.a n.a , , , , , , , , , , , , , , , , , , , , , ,03-1,77-0,28 Source: INE and authors calculations

136 The exports of manufactured products of cork in quantity have shown a remarkable constancy between 2004 and 2011, although with some large changes in between. However, the firms outside the region of Santa Maria da Feira behaved better, with a slight increase in this period of almost 2%. Table 4. Exports of the Cork industry, values Year S.M. Feira Other Regions Portugal R.Ch. (%) R.Ch. (%) R.Ch. (%) n.a n.a n.a , , , , , , , , , , , , , , , , , , , , , , , ,51 Source: INE and authors calculations The trends in the values exported are similar, but now with a slight decrease in all the regions, once more with a worst performance of Feira s firms. It is important to note that these are nominal values, and so the performance is much worrying in terms of real decay. It was indeed a difficult period for the Portuguese cork firms, and all things seen and pondered it appears that clustering the manufacturing activity in one region does not bring clear economic advantages, so the roots of the Feira cork cluster must be found in other (non-economic) domains. 4. The historical roots of the Feira Cluster in the production of cork The Iberian Peninsula reveals soil and climate conditions that give to Portugal and Spain an absolute competitive advantage in the production of cork. The western Mediterranean Basin presents optimal natural conditions for hosting cork oak and in particular, the Southwest of Iberian Peninsula emerges as the most important region in terms of the area occupied by this forest tree for almost two centuries (Aronson, Pereira and Pausas 2009: 13), being Portugal the world leader in the area of the specie and in the production of cork (APCOR 2011). The Portuguese cork sector presents two historical features: first, since the beginning the cork business is an export business; second, the cork industry has always presented a high geographical concentration. The export vocation of the sector signifies that globalization and its rhythms always affected the cork business, representing an external factor always challenging the survival of the firms, accustomed to play the game of internationalization. Furthermore, cork is a natural renewable resource used in stoppers and external demand is essentially limited to the countries that produce wine. The manufacturing of cork at least until the invention of the agglomerated cork in the nineteenth century was a labour-intensive process, making easier to later industrialised countries to develop the cork industry. Being a low-tech sector, dependent from a natural renewable resource, the firms in the cork industry were subordinated to two constraints in terms of their localization: proximity to natural raw material and cheap labour. Nevertheless, the motivations underpinning the strategic options of location of cork firms were also constrained by the type of cork industry production (Zapata 1996): cork planks, cork stoppers or agglomerated cork. 135

137 136 In the nineteenth century, Portugal exported cork planks, a semi-manufactured product and the first firms were settled in the South of the country, closer to the raw material, being much more expensive the transportation of raw cork comparatively to the labour costs (Mendes 2009). The developing of the stoppers industry and the agglomerates industry, plus the growing of cork exports (Mendes 2009), laid other constrains, namely, the proximity to maritime ports and to a more specialised labour force, changing the geography of new cork companies. During the process, the Centre of Portugal has become a growth pole of the cork industry for a long time, especially Setúbal, attracting international firms, like Mundet (1905), that became one of the largest cork companies in the world (Carrasco et al. 2010). The proximity to the Lisbon harbour and industrial labour force, gave to Setúbal the localization advantage for firms that produced manufactured cork (stoppers and agglomerates). Nevertheless, the presence of industries that produced agglomerate cork put Setúbal in a more vulnerable position in terms of international competition and, in the future, more vulnerable to the technological innovation that would arise, the plastics. However, according to the Boletim do Trabalho Industrial (DGT 1917), in 1917 the stoppers industry in Aveiro district had 43 establishments (41 in Feira) and 880 workers (368 in Feira), being already one of the most representative district in that industry. This means that the origin of the cluster can be put in the beginning of the twentieth century. But up to the 1930s, the leadership in terms of manufactured cork exports belonged to Spain, position ensured by the Catalonia cork industry. Still, the cork business was still dominated by developed countries like England, Germany and United States, which having not raw material, had high specialized labour, technology, capital and international trade power. The Spanish Civil War ( ) set a turning point in the Iberian cork business that pave the way to the Portuguese domination of the cork trade. Together, the Great Depression, the Civil War and the undertaken of Franco s Regime, settle the downturn of Spanish cork presence in worldwide trade (Branco e Parejo 2008). The opportunity wasn t frozen neither by the Portuguese government nor by the Portuguese entrepreneurs, but for the time being without any relevant change in the productive and trade specialization: the semi-manufactured cork still was the most relevant cork export until the 1950s. During the 1960s, a second important exogenous factor benefited again the Portuguese cork business, giving the final push to what would become the cork cluster in Santa Maria da Feira: the synthetic materials that substitute natural cork. Three main consequences resulted from that technologic innovation. First, the more developed countries abandoned the cork industry that came to be concentrated in the Iberian Peninsula. Second, these two countries became specialized in the production of cork stoppers, changing the outlet of cork products, now totally dominated by the wine producers. Thirdly, and resulting from the two previous, the Iberization of the cork business took place, that is, production, industry and trade, have become concentrated in the Iberian Peninsula (Zapata 2002; Zapata et al. 2009). In the context of the Iberization of cork business, Portugal and Spain had the advantaged in terms of abundant raw material. But this time, the roles of Portugal and Spain in the cork business were reversed: Portugal displaced Spain from her hegemonic position and became the worldwide leader of cork business, this time with a new specialization: manufactured cork (Parejo 2010). During this period the geographical pattern of this industry changed and the North of Portugal, namely Santa Maria da Feira, has become the capital of cork stoppers. Several authors have classified Santa Maria da Feira as an Industrial District or a Cluster and we can point the 1960s as the period when the cluster developed (Mira 1994; Ruivo 1992, 1995, 1996, Branco e Parejo 2011). The Boletim da Junta Nacional da Cortiça (1970), one of the most important publications about the cork sector, confirms the ascension of Aveiro, classifying this district as the most important in terms of manufactured cork.

138 Following Porter (1991) and the determinants of competitiveness presented in his diamond model, we can find in the origin and development of the Santa Maria da Feira Cluster two main key factors, locally and historically determined: the presence of craft workshops with skilled and cheap workers and the presence of an anchor firm. The combination of these two factors would never produce such results if it wasn t for the presence of one random exogenous factor: the Spanish Civil War that weakened the most important Portuguese competitor in the cork business, Spain. According to Belussi and Sedita (2009) the factor condition and the anchor firm (or firms) are always linked to the previous industrial history of a cluster. In the case of Feira, the two exogenous factors were transformed into an opportunity to the cork industry located there, boosted by an institutional framework that favoured some of the most important endogenous factors of Feira. The endowment of natural resources available was not an endogenous triggering factor for the cluster emergence since most of the cork production is concentrated in the South of Portugal, being Alentejo the leader. However, Feira already had an industrial tradition based on craft workshops producing stoppers. According to Mendes (2009) since the end of the nineteenth century the firms located in Feira were small family businesses. This feature was reinforced by the public (Estado Novo) industrial policy or, at least, not contradicted by one of the most significant measures of industrial policy, the Condicionamento Industrial that gave licences for a growing number of craft workshops in Aveiro district (Branco e Parejo 2011). Another important endogenous factor, again connected with institutional aspects, was the low wages paid in the North, Aveiro included. The cork workers in Santa Maria da Feira were the worst paid in the country, a situation reinforced by several laws that regulated the wages of cork manpower (Branco e Parejo 2011) and setting another competitive advantage for Portugal, besides the raw material. Sampaio (1982) shows that cheap labour is a relevant factor in terms of competitiveness and the lower costs made all the difference in competing with other Portuguese regions, namely Setúbal, since the instauration of the democratic process in Portugal was marked by the rising trend in wages. Finally, we can add the presence of a successfully anchor firm. Since its origin, the Cluster of Feira is an open local/global system although without any multinational company, a different feature from the clustering of firms in Setúbal. But Feira had a local anchor firm Amorim&Irmãos 72 whose history is confounded with the cluster history. Acting as an anchor firm and by taking a putting-out strategy, Amorim&Irmãos stimulated the spin-offs and new firms stars-ups (Branco e Parejo 2011). The Amorim&Irmãos had financially encouraged its workers to open small workshops and developed with them intensive ties. The relation with small stopper producers allowed the company to respond to fluctuations in the world demand of cork products without increasing the scale of production. Also in a Michael Porter s study about the cork cluster (Monitor Company 1994: 74, 135), he stated that the success of the cluster was explained by the small firms access to certain phases of production process that were provided by the larger company, Corticeira Amorim. Finally, the vertical integration and the diversification of markets and products strategies implemented by this company explain the success of Grupo Amorim (Branco and Parejo 2011) and the quasi monopoly position acquired by the group in the cork business: 26% of market share in the world wide of cork; 65% of market share in the cork stoppers and 55% of market share in the composite agglomerate and 80% of expanded agglomerate (Amorim 2011). The internationalization strategies of Amorim & Irmãos, Lda gave to Portugal a favourable position in the cork trade in 1986, when Portugal and Spain accessed to the European Union (EU). During the 1980s two tendencies were reinforced: the Europeanization of demand the important role of countries belonging to the EU that are wine producers, namely France and Italy - and the growing The first firm of the Grupo Amorim was founded in 1922, the Amorim & Irmãos, Lda., being the origin of Corticeira Amorim, founded in 1963.

139 importance of trade between Portugal and Spain this last country exports for Portugal mainly semimanufactured cork (Zapata et al. 2009). These final features may pose a threat to the success of Feira cluster because they open doors to a lock-in situation, not only in terms of mono-specialization in stoppers but also in terms of trade partners, giving to these a growing power of negotiation that could flat the export prices. Furthermore, the big firm strategies to face this threat could conduce to the demand of new suppliers outside the cluster in order to react to the lower prices pressure, since the wages are getting higher in Santa Maria da Feira Cluster Concluding remarks This paper studied the relative performance of clustered and non clustered firms in the Portuguese cork industry, and the historical and socio-political roots of the Feira s cork cluster, also known in the literature as the Aveiro s cork industrial district. The Portuguese cork industry provides an interesting case study on several levels. First of all, it must be stressed that Portugal is the world leading country in this economic activity, in terms of production, value added and employment, as well as international trade (representing more than 60% of all world cork exports). Moreover, there is a considerable geographic concentration of production in the municipality of Santa Maria da Feira, in what could be considered an industrial district, suitable for analysing and testing the Marshallian agglomeration economies and the competitive advantages of a cluster, as evidenced by Becattini and Porter, among many others. However, from the analysis of several mesoeconomic indicators quantified in this paper, at the regional level, the main conclusion is that there is no empirical evidence that unequivocally supports the economic advantages from the geographic concentration of the production of cork. In fact, the economic performance of clustered firms in Santa Maria da Feira is not significantly better than the performance of the correlative cork manufacturing firms dispersed in other regions of the country, and this applies both to relative labour productivity as well as export growth. This important result suggests that the effective concentration of the manufacturing cork activities in Portugal, since at least five or six decades ago, must be searched and found on other dimensions, not only or predominantly economic, but of historical, political and socio-institutional nature. As it is carefully explained in the main section of this paper, the cork cluster of Santa Maria da Feira is a mature one, whose formation has its roots in the beginning of the twentieth century and its development in the second half of this century, associated with the installation in this geographical area of a key anchor firm, Corticeira Amorim. It is a mono-cluster exploring a sector in which Portugal has absolute advantages based in the availability of its main raw material (natural cork) and the comparatively lower wages of an abundant and skilled labour force. The relations between the companies forming this cluster are also based on historical, cultural and social traditions of this region of Portugal that are not so strong in other regions, namely Alentejo and Algarve. This last advantage, which can subsume into a comprehensive conception of the notion of social capital, that is, for obvious reasons, very difficult to assess quantitatively, is certainly one that has justified the continuation and strengthening of this cluster in the past, and can continue to provide its resilience in the future. Two main research questions need to be further addressed in the future. The first is to make a comparative analysis of the economic performance of clustered and non clustered firms along a much larger time horizon, connecting it with the different phases of the cork industry, and the Feira cluster, life cycle. The second, is to develop a qualitative analysis of the knowledge and research networks, social capital, skill improvements, technological innovations, etc., that can be crucial to sustain the relative strength and world dominance of the cluster, and to avoid the eventual, and

142 FUNDOS ESTRUTURAIS EUROPEUS, CARACTERÍSTICAS REGIONAIS E CRESCIMENTO Carlos Pinho, Celeste Varum, Micaela Antunes GOVCOPP and Department of Economics, Management and Industrial Engineering (DEGEI), University of Aveiro, Campus Universitário de Santiago, Aveiro, Portugal. Resumo. O aprofundamento da coesão económica, social e territorial é um objectivo central da UE. Contudo, as disparidades existentes ao nível das regiões europeias são consideráveis, e há dúvidas se eventualmente se poderão dissipar. Alguma literatura recente tem analisado a eficiência dos Fundos Europeus para a promoção do crescimento e a redução das disparidades regionais entre os Membros. Neste artigo contribuímos para a literatura, examinando as condições sob as quais a ajuda financeira europeia poderá afectar o crescimento regional de forma eficiente. Para tal, exploram-se as interacções implícitas entre as transferências e o rendimento e outras características regionais, como a inovação ou o capital humano. O estudo é aplicado a um painel de 138 regiões europeias no período As conclusões sugerem uma eficiência maior dos Fundos em regiões mais pobres. Uma ideia adicional é a de que a utilização dos Fundos para intensificar o capital humano é aparentemente a melhor alternativa quando o objectivo é a promoção do crescimento regional e a redução das assimetrias regionais. Palavras-chave: Dados em painel, Fundos Estruturais, Crescimento económico regional EUROPEAN STRUCTURAL FUNDS, REGIONAL CHARACTERISTICS AND GROWTH Abstract. Strengthening economic, social and territorial cohesion is a central objective of the EU. However, disparities among European regions are considerable, and there are doubts if they are likely to reduce. In recent years there has been an increasing literature examining the effectiveness of the EU funds for promoting growth and reducing the disparities between Members. In this article we contribute to the literature by examining under which conditions EU financial aid may be affecting regional growth. Doing so, it explores the implicit interactions between transfers and income and other regional characteristics, such as innovation or human capital. The study is applied to a panel of 138 European regions covering the period The conclusions suggest a higher efficiency of the funds in poorer regions. Moreover, the use of funds to strengthening human capital is apparently the best alternative when the aim is to promote regional growth and reduce regional disparities. Keywords: Panel data, Regional economic growth, Structural Funds 1. INTRODUCTION In a recent reflection about the Annual Growth Survey of the European Union, the Council of the European Union (2013) emphasised the need to ensure that actions at the EU level fully support economic growth, job creation and reduction of regional disparities. It is stressed that the focus of the EU budget should lie in throughout measures enabling smart, sustainable and inclusive growth, according to the Europe 2020 growth strategy. Strengthening economic, social and territorial cohesion by reducing disparities between its regions is a central objective of the EU laid down in its Treaty. In spite of this, the first mention to a formal regional policy occurred in the Single European Act (1986). Since then the EU has completed three programming periods ( , , ) and is currently closing the fourth ( ). For the latter, funding for regional and cohesion policy represented 35.7 per cent of EU budget, which reveals the central role of the regional policy in the EU agenda. For the next programming period ( ), Structural Funds will increase the focus upon innovation and smart growth specialisation, in line with the increasing importance of education and of the innovative activity for growth. Given 141

143 142 the current constraints on national public funding, this orientation of Funds appears as crucial to fill the gaps on national investments. An intense debate, lasting for decades, questions the effectiveness of Structural Funds for achieving the EU goals of economic growth, disparities reduction and social and economic cohesion. Indeed, it is not clear whether the promotion of regional competitiveness is compatible with the goal of reducing regional asymmetries. On the one hand, financial aid, instead of reducing disparities, leads to centripetal forces that favour the centre in detriment of peripheral areas (Llussá and Lopes, 2011). On the other hand, many regions that have been major net beneficiaries of financial aid have not grown beyond the threshold of assistance, raising doubts about the efficiency of the regional policy. Additionally, the presence of substitution effects may divert factors from more productive projects to those financed by Structural Funds (Kyriacou and Roca-Sagalés, 2012). The 2004 enlargement of the EU came to challenge even further the EU regional policy, given the higher heterogeneity it encompasses. On the one hand, the Cohesion countries highly dependent on EU financial assistance and, on the other, the new member states asking for transfers to tackle structural difficulties, dispute limited financial resources. The constraints upon the available resources, the sizeable needs and the different challenges regions face in the context of increasing globalisation, may hamper the functioning of the regional policy. Since the first support programming period, the European Commission has emphasised that European policy assistance to regions should be subject to regular and rigorous evaluation. When doing so, it is well-established in the literature that one should not expect absolute convergence between regions. Indeed, the economic understanding of these matters has evolved also quite substantially as a result of valuable contributions (see, for instance, Barro and Sala-i-Martin, 2004). The neoclassical approach to convergence is derived from the Solow s growth (1956) model with diminishing marginal returns to capital and exogenous technical progress. The idea was that poorer economies would tend to grow faster than richer ones in earlier stages of economic development and then in the long-run they would grow at similar growth rates, due to the law of diminishing returns to capital. Given the unsatisfactory results provided by absolute convergence, a new concept of convergence emerged: conditional convergence, developed under the framework of endogenous growth theory. According to this approach, convergence is assumed to be conditioned by some structural factors with increasing returns to scale properties, coming mostly from human and physical capital accumulation, technological progress and innovation. Therefore, economies converge to different steady-states determined by idiosyncratic characteristics (Barro and Sala-i-Martin, 2004). From the early 1990s onwards there has been an upsurge in the empirical literature regarding growth and convergence, both at the national and regional levels. However, given the diversity of methodological approaches, time spans and sets of explanatory variables, the conclusions are far from being unanimous, even regarding the specific case of the European Union. 73 The focus upon European regional economic growth gained increased relevance after the enlargement to the central and eastern European countries. The inclusion of these economies, along with the increasing budget constraints, has brought to the spotlight the discussion about the reduction of regional disparities and the ability of the EU policies in general, and of the regional assistance in specific, to effectively promote and guarantee social and economic cohesion among its members. Within this context, this study contributes to the literature by analysing the impact of Structural Funds on growth for the period. The dataset includes also the new EU members from the 2004 enlargement (Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Malta, Poland, Slovakia and Slovenia), where data is available. We examine the (indirect) channels through which Structural Funds possibly affect growth, by analysing the links with income, human capital and 73 See for instance Henrekson et al. (1997), defending the existence of growth effects from EU integration and Vanhoudt (1999), claiming the existence of only trade effects.

144 innovation. Only a few studies on this subject have included interaction terms, although in a different perspective. Regarding the most recent, Llussá and Lopes (2011) interacted income with each of the Objective dummies (to examine how eligibility contributes to growth) and they verified that being covered by one of EU regional policy contributes to regional convergence, though with a relatively weak effect. Rodriguez-Pose and Novak (2011) combined regional per capita Funds either with national per capita GDP or with relative regional per capita GDP, to determine Structural Funds effectiveness depending on national and relative wealth, respectively. Contrary to the outcomes for , for the authors conclude that the returns of Funds tend to be greater the wealthier the country and also the better the region s relative position within a country. Finally, we also try to understand if the sign of the impact of the Funds is always the same, regardless of the amount received by a region (both in per capita terms and as a percentage of GDP) or, instead, if there is a given threshold from which the returns are reversed. This addresses the discussion about the effectiveness of financial transfers and conditions under which more Funds are reflected into more growth. The outline of the study is the following: in Section 1 we reflect upon the role of Structural Funds, human capital and innovation on regional growth and discuss central results from the literature. In Section 2 we present the growth model and the variables used. Section 3 discusses the main outcomes and the last section concludes. 2. THE ROLE OF STRUCTURAL FUNDS, HUMAN CAPITAL AND INNOVATION ON REGIONAL GROWTH The role of Structural Funds is at the centre of the discussion on the effectiveness of the EU regional policy to attain the desired goals of growth, competitiveness, disparities reduction and economic, social and (more recently) territorial cohesion. The bulk of literature on this matter has been developed under the framework of the neoclassical growth model (e.g. Cappelen et al., 2003; Esposti and Bussoletti, 2008; Rodriguez-Pose and Novak, 2011). The results differ significantly in several aspects: the indicator used for Structural Funds, the time period and the sample analysed, and the estimation method applied. Moreover, poor quality data on Structural Funds also contributes to major differences in the outcomes. While some studies point to the existence of positive effects of Funds on growth (Cappelen et al., 2003; Ramajo et al., 2008; Becker et al., 2010), others find inconclusive impacts (Rodriguez-Pose and Fratesi, 2004; Mohl and Hagen, 2010) or even negative ones (Dall erba and Le Gallo, 2008). 74 Moreover, Llussá and Lopes (2011) find that financial assistance from the EU contributed to increase convergence only at the national level. Rodriguez-Pose and Novak (2011) find the existence of learning processes between the second and third programming periods that resulted in a more effective allocation and monitoring of expenditures on Structural Funds, thus impacting positively on regional growth. Bearing in mind the discussion presented and the variety of conclusions, we explore the effect of the structural funds. A positive effect would go along those arguments defending the relevance of structural aid to help deprived regions and reduce regional disparities. Otherwise, a negative effect would question whether EU regional policy has been oriented in the right direction and whether it has been efficiently allocated. In addition in this article we explore the way in which EU transfers work, i.e, if there is a direct link or, otherwise, a channel transmission is present, and if the effect depends on other contextual variables. Moreover, we check for the existence of a limit upon which financial transfers no long exert the desired positive impact on growth. Indeed, regions differ along several endogenous characteristics that matter for growth, and these may interact with the effects of the EU funding upon growth. On this regard, the models of endogenous growth confer a special role to human capital. The latter is a measure of the ability and skills of the labour force, and it is evaluated by the level of formal education or by job accumulated For a comparison of the major results on the literature about the impact of Structural Funds on economic growth, see Mohl and Hagen (2010).

145 experience, with potential to contribute to increase the productivity of physical capital (Lucas, 1988). A better-educated and well-trained workforce is expected to exert a positive effect on growth. 75 However, the empirical results have sometimes shown a different pattern, with the impact of human capital on growth being negative and/or statistically insignificant, especially in panel data studies. Islam (1995) and others argue that this happens due to poor quality data and inadequate proxies to capture qualitative rather than quantitative aspects of human capital. According to another line of research, it is the accumulation of technological change the key factor for growth (Romer, 1986; Di Liberto, 2005). On the one hand, technology is non-rival, which implies the existence of knowledge spillovers, increasing returns and externalities. 76 On the other hand, nonexcludability depends both on the kind of knowledge produced and on the mechanisms protecting property rights, like the patents. The patents system as an incentive to protect the R&D sector is thus determinant for growth (Grossman and Helpman, 1991 b). Human capital and innovation are reflected into faster economic growth only if economies show the social capability to benefit from higher education skills and more protection to R&D (Abramovitz, 1986; Crescenzi and Rodriguez-Pose, 2012). 77 Bearing this in mind, Alexiadis et al. (2010) reinforce the need for the European policy to focus on R&D activities in order to promote regional growth in an enlarged area. This corroborates the target of investing 3 per cent of GDP in R&D under the European 2020 growth strategy. Hence, from the previous considerations, we expect both human capital and innovation to play a positive role. Furthermore we explore how (and if) the regional conditions on these matters condition the effect of the EU assistance upon regional growth. 3. DESCRIPTION OF THE MODEL AND THE DATA USED In order to address our research issue, we consider an augmented version of the neoclassical growth model to panel data, to avoid omitted variable bias. The growth equation to be estimated is the following: gy i,t = bln 4 y +c ln(gpop ) +c ln(s ) ln(s ) i,t 2 c ln(pat i,t 2 1 )+c ln(sf 5 i,t 2 i,t 2 2 ) v i,t, i,t 2 with v i,t i,t 3 i +c ln(hc 3 = α + u where α i refers to country-specific effects, λt reveals time-specific effects common to all units and u i,t is the idiosyncratic error term. The subscript i refers to regions 78 and t is the time index, ranging from 1995 to Although the time period is relatively small, it is still adequate for growth regressions and inferences can be made for a medium-term horizon. The dependent variable is the annual growth rate of real per capita income ( ). 79 The set of explanatory variables includes:, real per capita income;, annual population growth rate;, growth of the investment share;, human capital measured by the ratio of students in tertiary education over year-olds;, innovation proxied by the number of patents per million inhabitants; 80 and, (interpolated) t i,t i,t 2 )+ (1) See, for instance, Ciccone and Papaioannou (2006). 76 See Grossman and Helpman (1991 a). 77 As an example of a study on the impact of innovation on regional growth, see Fagerberg et al. (1997). For the joint impact of human capital and innovative performance, see Rodriguez-Pose and Crescenzi (2006). 78 For a detailed description of the 138 regions included, see the Appendix I. 79 For the description and explanation on the computation of the variables, see the Appendix II. ln stands for natural logarithms. 80 We alternatively used the share of R&D expenditures on GDP, but the results were generally more fragile and this variable never displayed statistical significance.

146 Structural Funds. 81 All the explanatory variables are lagged twice, to avoid endogeneity and reverse causality EMPIRICAL RESULTS We estimate the model by FE 83 using Rogers standard errors, which are heteroscedasticity and autocorrelation consistent. 84 The limitation of this approach is that it does not take into account the possible existence of cross-sectional correlations, i.e, it assumes that residuals are correlated within but not between groups of individuals. Conversely, Driscoll and Kraay s approach deals with an error structure that is assumed to be heteroscedastic, autocorrelated up to some lag and possibly correlated between groups. However, with this correction standard errors are robust to general forms of spatial and temporal dependence only when T (Baltagi, 2005). Moreover, only strong cross-sectional correlation constitutes a serious problem in terms of estimation and inference (Pesaran, 2012). Still, as robustness check we also use Driscoll and Kraay s standard errors following Hoechle (2007). 85 Table 1 displays the results of the estimation of the growth equation with per capita Structural Funds as the proxy for financial assistance. Both FE with robust standard errors and FE with standard errors from Driscoll and Kraay s correction are presented for comparison purposes. The difference between them is in the standard errors and thus statistical significance of the variables may change. All the estimations include time dummies. Table 1. FE with robust and Driscoll-Kraay's standard errors. 138 European regions, (Structural Funds per capita) Notes: Coefficient significant at the 10 per cent (*), 5 per cent (**) or 1 per cent level (***) 81 We use per capita real Structural Funds,, and alternatively, Structural Funds as a percentage of GDP,. Since some values are null, to avoid losing observations we add 1 to the Funds before computing the logarithm. 82 Rodridguez-Pose and Fratesi (2004) also use two-year lagged income to avoid endogeneity. 83 The regressions were run using Stata It is often more adequate to use FE than RE with aggregated data, in terms of policy analysis (Wooldridge, 2009). 85 We are not able to perform Pesaran s CD test on residuals cross-sectional dependence after the FE regression (De Hoyos and Sarafidis, 2006) for not having enough common observations in the panel. We thus opted for comparing outcomes from both methods. 145

147 Common to all the estimations presented is the evidence of conditional convergence, given the negative and significant sign of the convergence coefficient. The evolution of physical capital is almost always significant with the expected positive sign. 86 Moreover, annual population growth displays a negative coefficient and the impact over income growth is insignificant, a common finding in the literature. Human capital is positively and significantly influencing growth, as expected. This is the only variable that maintains its significance in all the estimations ran and it exerts the highest impact on growth. The patents ratio is rarely significant, but removing it from the estimations does not improve overall results and by keeping it we avoid the omitted variable bias. Finally, Structural Funds also show a positive impact on growth, although not always significant. When considered alone, financial assistance is not significantly affecting growth ((1) and (2)). In columns (3) and (4) we add an interaction term between income and transfers, to assert whether the effectiveness of Structural Funds depends on the development level of the region. With the correction for spatial dependence (column (4)) both the Funds and the interaction term are not significant. Hence, we base our interpretation in the results from column (3). The impact of Structural Funds on growth is given by: Thus, the Funds have a positive impact on growth for regions with per capita incomes lower than the limit here identified. This outcome goes in line with the findings of Ramajo et al. (2008) and Mohl and Hagen (2010), supporting the idea that EU regional policy explicitly oriented for the less developed areas is most effective in promoting growth. In columns (5) and (6) we test the interaction effect between innovation and Structural Funds. When no cross-sectional dependence is accounted for (column (5)) all the elements of interest are significant. In that case, Structural Funds influence growth according to: Thus, we conclude that Funds exert a positive impact on growth on regions showing lower levels of innovation. Given that innovation and income levels are generally positively correlated, this outcome demonstrates once more that the returns to EU financial assistance are higher in less favoured areas. In columns (7) and (8) the Structural Funds were interacted with human capital. Only with the Driscoll and Kraay s correction the multiplicative effect is significant (column (8)). The impact of the Funds is, in that case, given by: 146 Structural Funds exert a positive and significant effect on growth whenever the share of people with tertiary education is lower than the indicated limit. Human capital is positively correlated with the 86 The alternative use of the lagged investment share resulted on a negative (and generally non-significant) impact.

148 level of income, showing (again) that the impact of the Funds is positive in regions with low per capita incomes. Finally, in columns (9) and (10) we are able to understand that the Funds affect growth positively, although at a diminishing marginal rate. In fact, the value of the Funds that maximises the impact over growth is approximately and from that point on, each additional Euro received per inhabitant will influence growth negatively, thus evidencing the existence of an inverted U- relationship between the variables. All in all, considering the variables that maintain their significance in each pair of estimations, we are able to summarise that: (i) human capital is the only variable robustly explaining growth across regressions; (ii) per capita Funds are not significant, meaning that at the most their impact depend on other conditionings; (iii) the effect of the Funds upon growth is more noticeable for less favoured regions; and (iv) the Funds affect growth positively at a decreasing marginal rate, changing sign beyond a certain threshold. In Table 2 we use as the Structural Funds proxy the share of Funds on GDP. Table 2. FE with robust and Driscoll-Kraay's standard errors. 138 European regions, (Structural Funds as a percentage of GDP) Notes: Coefficient significant at the 10 per cent (*), 5 per cent (**) or 1 per cent level (***) Once more, there is evidence of conditional convergence and the population growth does not have a significant impact. In addition, the human capital proxy is always statistically significant, displaying the expected positive sign. Physical capital and innovation are also positive, but there are some cases of statistical insignificance - though less than in the previous table. Regarding our variable of interest, the Funds are rarely significant. In fact, in columns (1) and (2), the impact is even negative, but only significant in the first case. The inclusion of an interactive term between Funds and income level (columns (3) and (4)) does not improve the results. When the patents are interacted with the Funds, in columns (5) and (6), the direct impact of Funds is not statistically significant. Thus, we are not in the presence of a direct but only of an indirect effect on the part of EU transfers. The Funds positively influence growth as long as the patents ratio is lower than 1. Since poorer regions innovate less, this shows that disadvantaged regions assist to a positive impact of financial aid on growth. 147

149 By interacting human capital with Structural Funds (columns (7) and (8)), we conclude that Funds positively and significantly affect growth if the share of people with tertiary education is lower than 27.7 per cent. Like previously, the impact of Funds on growth is thus positive in poorer regions with lower human capital endowment. From the two last columns ((9) and (10)), we get to the conclusion that the Funds influence growth positively but at a decreasing rate. For shares higher than 1.6 per cent of GDP, the positive effect of Structural Funds is reversed. To the best of our knowledge, no threshold had been established at the regional level so far. This outcome is equal to the threshold found by Kyriacou and Roca-Sagalés (2012) at the national level. Additionally, this result focuses the importance of the ceiling (of 4 per cent) that was established in the Lisbon Treaty, for the country s total transfers. Reflecting on the common elements from Table 2, we conclude that: (i) the effect of the Funds share on GDP is not related to the region s income level; (ii) the Funds display a positive role on growth in poorer less-innovative regions; (iii) the Funds share is successfully combined with human capital and plays a positive role if human capital is lower than a certain level, showing that in less developed areas with low levels of education the returns from Funds are probably higher; and (iv) for regional transfer intensities above 1.6 per cent, the effect of Structural Funds on growth is potentially reversed. Combining the information from the tables, there is clear evidence of conditional convergence and human capital is always significant for growth, displaying the expected positive sign. The annual population growth is not significant and the evolution of the investment share reasonably and positively influences growth, being more proper when the share of Funds is used. The innovation proxy is not always significant, but it also displays the expected positive sign. As for the Structural Funds proxy, the conclusions differ, depending on the indicator used CONCLUSION It is clear that funding needs to be large enough to make a difference which implies it should not be spread too widely across policy areas. But because regions differ in terms of the problems they face and their specific needs, which are hard to identify centrally, there is a case for allowing them to choose a limited number of areas on which to concentrate funds. There is an extensive research on the impact of Structural Funds to reduce EU regional asymmetries and to promote sustained growth. However, the outcomes vary widely and it is hard to establish comparisons. We find evidence of conditional convergence. Moreover, human capital is always significant and it positively affects growth. Both the population growth rate and innovation display mixed results, depending on the estimation method and the combination of explanatory variables. The evolution of investment is positive but not always significant. Regarding our variable of interest - Structural Funds -, the results differ depending on the proxy used and the estimation method. For per capita Funds, we observe that they do not act significantly when considered isolated. Thus, by adding interaction terms we find the mechanisms through which this variable exerts impact on growth. This way, the impact of the EU transfers depends on the level of income, innovation or human capital. Comparing the results for each pair of regressions, the conclusions differ depending on the estimation method considered. Apparently, the Funds have a positive impact on growth in lower-income, less innovative regions with low human capital. This result raises questions about the effectiveness of the Funds, originally designed to assist deprived regions to flourish and thus reduce regional asymmetries. On the one hand the finding support the idea that EU regional policy should explicitly be oriented for the less developed areas, if the aim is to be most effective in promoting growth. On the other hand, less developed regions may not have the right incentives to go beyond the threshold of assistance. In addition, the Funds affect growth positively at a decreasing marginal rate; above a certain limit the sign of the impact is reversed, thus calling the attention for the

150 plausible limits regarding financial assistance destined to promoting growth and reducing regional asymmetries. When the share of Funds on GDP is considered, the transfers are reasonably combined with innovation and human capital. For the former, Funds impact indirectly on growth, showing a positive impact in less innovative areas. For the latter, the impact of Funds is only positive if the share of people with tertiary education is lower than 27.7 per cent. Thus, apparently the Funds are more effective and display higher returns in regions with low indicators regarding human capital. This result support the focus upon less developed regions, but this approach creates a moral hazard problem, due to the lack of incentives to improve human capital skills because it will mean losing Funds. Human capital is always significantly and positively affecting growth and this fact should act as an incentive for policy makers to incentive improvements in this field. In addition, for regional transfer intensities above 1.6 per cent, the effect of Structural Funds on growth becomes negative, thus highlighting the need for the existence of reasonable limits of assistance, beyond which it loses effectiveness. Summing up, the debate about the importance of Structural Funds is not conciliatory and our results show once more that the discussion on this matter is not plain. The outcomes found point in general to a positive impact of Funds over growth especially in less developed regions, and up to a certain level of assistance. At the same time, our results raise questions about the effectiveness of Structural Funds, since that they may originate behaviours from policy makers contrary to those originally intended. Considering the direct impact of human capital upon growth, financial aid destined to improve human capital qualifications is probably the best way to promote growth in the long run, rather than allocating it mainly to physical infrastructures. This goes in line with the EU s Agenda 2020 focus on smart, sustainable and inclusive growth, by improving school early leavers figures, investing in R&D and young employment, reducing the population affected by poverty and promoting energy efficiency. We are aware that some of the effects of Structural Funds may take time to be visible, but our main goal was to ascertain the links through which Funds might be affecting growth, to assist in the decision of which policy to favour. This is even more important given the changes that are currently being discussed regarding the next programming period ( ). Appendix I. Construction of the dataset The choice on the level of regional disaggregation (NUTS) 87 for each country depended on the availability of data for Structural Funds. For the years , data on Structural Funds payments was collected from the European Commission s annual Reports (European Commission, 1996; 1997; 1998; 1999). For 1999, we computed the payments as the residual difference between commitments and payments in the period. 88 From 2000 onwards, we relied on data sent on 12 th December 2012 by the European Commission DG Regional and Urban Policy, following a formal request. The estimated payments for refer to the programmes ERDF (European Regional Development Fund), ESF (European Social Fund), EAGGF (European Agricultural Guidance and Guarantee Fund), FIFG (Financial Instrument for Fisheries Guidance) and Cohesion Fund. For , payments concern funds received under ERDF, ESF and the Cohesion Fund. The information is disaggregated at the NUTS2 level. For some countries, data on the Reports is available only for NUTS1 and thus we had to reconcile these different NUTS, a territorial classification from the European Commission, means Nomenclature des Unités Territoriales Statistiques. 88 We could not successfully download the European Commission s 11th Annual Report on the Structural Funds 1999 (published in 2000), from:

156 ARE SMALL FIRMS MORE DEPENDENT ON THE LOCAL ENVIRONMENT THAN LARGER FIRMS? EVIDENCE FROM PORTUGUESE MANUFACTURING FIRMS Carlos Carreira, Luís Lopes Faculdade de Economia/GEMF, Universidade de Coimbra; Av. Dias da Silva, 165; Coimbra, Portugal. Abstract. This paper analyses the impact on firm-level total factor productivity (TFP) of both agglomeration economies and regional knowledge, measured by local sector specialization and diversity, and of regional knowledge, using an unbalanced panel of Portuguese manufacturing firms covering the period Additionally, we study if smaller firms are more dependent of local environment than larger ones. We use TFP at firm level, which was estimated after controlling for endogenous input choices and self selection using the Arellano and Bond (1991) difference GMM estimator. We found that both localization and urbanization economies have a significant and positive effect on firms TFP, with the latter playing the most important role. Sectoral specialization economies are important for small and medium firms, but not for large firms. However, larger firms profit more from regional knowledge than smaller ones. Keywords: agglomeration economies, regional knowledge, small firms, total factor productivity. Resumo. Analisa-se o efeito das economias de aglomeração e do conhecimento regional na produtividade total dos factores (TFP) ao nível da empresa, medidas pela especialização e diversificação sectorial e pelo conhecimento regional, utilizando um painel não balanceado de empresas da indústria transformadora, no período Analisa-se ainda se são as pequenas empresas as mais dependentes do ambiente local. Utilizamos a TFP ao nível das empresas, estimada depois de se controlar a endogeneidade associada às escolhas de inputs e à auto selecção com o estimador GMM de equações em primeiras diferenças de Arellano e Bond (1991). Conclui-se que as economias de localização e urbanização têm um efeito positivo e significante na TFP, sendo as de urbanização mais importantes. Conclui-se também que as economias de especialização sectorial são importantes para as pequenas e médias empresas, mas não para as grandes empresas, sendo estas as que mais beneficiam do conhecimento regional Palavras-chave: economias de aglomeração, conhecimento regional, pequenas empresas, produtividade total dos factores 1. Introduction The study of spatial agglomeration of both production activities and knowledge is important to understand their contribution for local and national economic growth. Notwithstanding the tendency to reducing transaction costs, there has been observed an increasing propensity for firms to agglomerate their activities in certain regions with economic impacts on employment levels, wages, knowledge, productivity and economic growth. The theories of the location of economic activity are microeconomic in his essence, which means that the empirical studies should use firm level data. However, the unavailability of large microeconomic datasets has favoured empirical investigations at the aggregate rather than micro-level. Moreover, given that productivity growth at firm level is generally not available, most of earlier studies use proxies, namely employment growth or wage growth, under the assumption that there is a national labour market and that labour is homogeneous, which is hardly verified. 155

157 In this paper, we perform a micro-level analysis of TFP to shed further light on the extent to which the local environment has an effect on firms performance. The main purpose of our paper is thus to estimate the impact of agglomerations economies and regional knowledge base, on firms TFP. Additionally, we study if smaller firms are more dependent of local environment than larger ones. To conduct the analysis, we will use an unbalanced panel of Portuguese manufacturing firms covering the period This paper makes two main contributions to the economic literature. Even though agglomeration economies and regional knowledge base encompass a large number of studies, to our awareness, there has been no research that assesses the role of these two productivity sources together. Furthermore, there is scarce evidence on the effect of local environment on firms TFP, specially, across firms size. The paper proceeds as follows. Following a brief review of the background literature in the next section, Section 3 presents the modelling and the dataset. Section 4 evaluates the effects of agglomeration economies and regional knowledgebase on firm growth across firms size. Section 5 offers some brief concluding remarks Theory and selected empirical findings The location of economic activity within the models of the new economic geography is endogenously determined through the interaction between two forces: the centripetal forces that attract economic agents to the same location and the centrifugal forces that push them apart (Krugman, 1998). Externalities, a key concept developed by Marshall, are the most important centripetal force, as they are central to explain why production activities tend to agglomerate in certain regions. 89 The rationale is that, in the process of choosing its spatial location, a firm looks for the proximity of other firms due to the benefits they can get. Glaeser et al. (1992) identifies three sources of externalities: i) Marshall-Arrow-Romer (MAR) after the three pioneering contributions of Marshall (1890/1961), Arrow (1962) and Romer (1986) or localization externalities, which are related to intra-industry economies arising from the regional concentration of firms in the same industry (i.e. sectoral specialization). Firms have advantages in being located near others belonging to the same industry because the geographical concentration of an industry can increase the variety of intermediate goods available (at lower prices) as well as the dimension of final good demand, can attract a large labour force with the skills demanded by that industry and can spread a great specialized knowledge level (namely via informal channels). ii) Jacobs or urbanization externalities, which are connected to inter-industry economies arising from the variety of regional economic activity (Jacobs 1969). A sectoral diversity in a given region can stimulate a more diverse client base protecting firms from volatile demand, can create a vast spectrum of locally available inputs easing their switching in case of scarcity or a rise in prices and can disseminate a more assorted knowledge base increasing the possibility of discovering new products or production processes. iii) Porter or competition externalities, which are related with competition intensity within a region. Competition stimulates both production and adoption of innovations and, consequently, improves firms performance (Porter, 1990). Porter externalities are similar to MAR externalities, but unlike earlier, it is local competition and not local monopoly that stimulate a faster search and adoption of innovations. As it is possible to see, the theories which underlie externalities, whether MAR-type, Jacobs type or Porter type, are microeconomic in essence, which means, that empirical studies should use firm level data. However, as generally data on productivity is generally not available, it is used as a proxy for 89 Krugman (1998) identify as the main centrifugal forces the immobile factors (e.g. certain land and natural resources), the high land rents and the external diseconomies (such as congestion).

158 productivity employment or wages data under the assumption that there is a national labour market and that labour is homogeneous and that productivity growth will result in proportional employment gains through shifts in labour demand, which is hardly verified (see, for example, Glaeser et al. (1992); Henderson et al. (1995); Combes (2000)). Using a cross-section of US cities, Glaeser et al. (1992) find that MAR externalities have a negative impact on urban employment growth, while Jacobs and Porter economies positively affect it. Glaeser et al. (1992) approach has been replicated by other authors (see, for example, Cingano and Schivardi (2004), for a brief survey). However, the findings from these researches are to some extent puzzling. Moreover, while taking local employment growth as the dependent variable, using 1991 Italian census data, Cingano and Schivardi (2004) show that the specialization effect is negative and variety has a significant and positive impact on employment growth, in line with Glaeser et al. s results, using firm-level based TFP indicators, they find that specialization effect is reversed and becomes positive, and neither sectoral variety nor the degree of local competition have any effect. Cingano and Schivardi (2004) question the conclusions of previous empirical works arguing that they suffer from serious identification problems when interpreted as evidence of dynamic externalities, since the chain of causality from agglomeration economies to employment growth could be reversed the use of employment or wages growth at firm level as dependent variable is based on the (unlikely) assumption that productivity growth will result in proportional employment gains through shifts in labour demand (see, for example, Glaeser et al. (1992); Henderson et al. (1995); Combes (2000)). Therefore, since externalities imply a change in output not fully accounted for by a change in inputs, TFP would be a better measure of performance. Martin et al. (2011) show that French plants from 1996 to 2004benefit in terms of TFP growth from localization, but not from urbanization economies. They do not find any consistent pattern for local competition. An explanation can be that competition incentives firms to invest in R&D, but if the succession of innovations is rapid, the returns from R&D are low, which will reduce the R&D investment and, as a consequence, the innovations. In the case of the USA plants, over the period , Henderson (2003) find that localization economies only have strong positive effects on TFP in high-tech not in mechanical industries. He also finds little evidence of urbanization economies. Another interesting strand of economic geography research, favoured by the flourishing endogenous growth theories, has pointed out that localized knowledge and technology spillovers matter for innovative activity, which is consequently shaped by space and concentrated in certain areas (Scott (1988); Feldman (1994); Acs (2002); Johansson and Lööf (2008); Bronzini and Piselli (2009)). In particular, it is argued that proximity to the knowledge base can encourage the circulation of ideas and the transmission of knowledge, thanks to face-to-face contacts and social interaction, which in turn facilitates innovation (Storper and Venables (2004); see Audretsch and Feldman (2004) for a review of theoretical and empirical studies). The knowledge-transfer environment in which a firm is embedded can also play a key role in explaining productivity differential between firms located in different geographic areas (Amesse and Cohendet (2001)) for example, knowledge intensive business services (KIBS) are crucial to disseminate knowledge across the region and to support firms innovative activity (Muller and Zenker (2001)). Looking at the firm size, in general small firms could be expected to be more dependent on the local environment than larger firms (Henderson (2003); Andersson and Lööf (2009)). Indeed, they are less able than large firms to internalise innovative inputs and providing complementary activities that may facilitate innovation (Feldman, 1994). On the whole, despite the fact that the literature on agglomeration economies and regional knowledge base encompass a large body of studies, to our awareness, there has been no empirical research that assesses the role of these two productivity sources together. In fact, if both factors affect productivity and interact with each other and if one these factors is omitted, estimations of elasticity can be biased. Moreover, there is scarce evidence on effect of local environment across firms size. We will try to fill this gap by assessing the role of both agglomeration economies and regional knowledgebase effects in enhancing the TFP by firm size. 157

159 3. Empirical strategy 3.1 The data To conduct our empirical analysis, we use an unbalanced panel of Portuguese manufacturing firms covering the period The raw data is drawn from the combination of two statistical data sources, both run by the Portuguese Statistical Office (INE): Inquérito às Empresas Harmonizado (IEH), an annual business survey with detailed information on inputs and output, required to determine productivity at firm level; and Ficheiro de Unidades Estatísticas (FUE) which contains a variety of firm characteristics (activity, number of employees, age and location) of all Portuguese firms, critical to compute spatial agglomeration variables. The longitudinal dimension of the panel was constructed using firm s unique identification code. The unit of production considered is the firm. Each firm is assigned to a given region (at NUT3 level; NUT3 definition of 2002) through a spatial identification code. Thus, the first drawback of the data is that multi-plant firms may affect our results if their different plants are located in different regions. We note, however, that the different plants of corporations are often registered as distinct legal entities, thus the multi-plant phenomena impact on results may be small. The IEH survey comprises all firms operating in Portugal with more than 100 employees and a representative random sample of firms with less than 100 employees. 90 For the purpose of this paper the following cleaning procedures were made: firstly, due to the lack of quality of information reported, firms with less than 20 employees were eliminated from the estimation sample; 91 secondly, both firms located in the island regions (i.e. Madeira and Azores) and, given the number of observations, those operating in manufacture of tobacco products (CAE 16) and manufacture of coke, refined petroleum products and nuclear fuel (CAE 23)were also excluded; finally, observations that were reported with either missing or unreasonable values (negative values and outliers) were dropped. For each industry, we define as an outlier a firm for which the log difference between an input and the output is in the top or bottom one percentile of the respective distribution. As a result of all these procedures, we have an unbalanced panel of 8,074 firms, over the period , resulting in 32,003 (year-firm) observations Empirical model and variables The main purpose of our analysis is thus to shed further light on the extent to which the local environment has an impact on productivity. In the past few years the study of this issue has greatly shifted from aggregated regional level towards the understanding of the operation of micro units (Stephan (2011); Ottaviano (2011)). Accordingly, the general model that we use for our empirical analysis is a firm-level Cobb-Douglas production function we assume that each firm is located in a given region and operates in a given industry: 158 Y it A K it j it L j it M j it, (1) where Y it is the real gross output of the ith firm and year t (located in region rand operating in industry j), and K it, L it and M it are capital, labour and material (intermediate) inputs, respectively; A it is the total factor productivity (TFP). We allow for the coefficients j, j and j to vary across industries. Given the regulation of the Portuguese labour market, we cannot assume perfect competition hypothesis, so neither constant returns to scale. The advantage is that, we disentangle TFP changes from production-scale effects, otherwise attributed to TFP. 90 The sample is representative of the Portuguese sector disaggregation (at 3-digit level), both in terms of employment size and sales. 91 We note that firms with less than 20 employees represent about 71% of Portuguese manufacturing firms, but only 16% of total employment (average over the period; source: OECD database).

160 The gross output is given by the sum of total revenues from sales, services rendered and production subsidies. It is deflated by the producer price index at the 3-digit level. The labour input is a 12- month employment average. Materials include the cost of materials and services purchased and were deflated by the GDP deflator. Capital stock is measured as the book value of total net assets (excluding financial investments and cash stock). We assume that TFP of firm iis driven not only by firm s knowledge, but also by both agglomeration economies and regional knowledgebase: A it jr R S Z jr it it it, (2) where R it is the firm s knowledge stock in year t, jr S it is a vector of covariates that reflects the potential for spatial agglomeration economies of industry j in region r, and covariates that proxies regional knowledgebase. We assume as a proxy for firm s stock of knowledge the inverse of firm s size times its age: FKNOW it 1 Lit Ageit. (3) jr Z it is a vector of The rationale is that older and larger firms often command more resources and have higher managerial experience (Jovanovic (1982)). The firm s knowledge returns are assumed non-linear and decreasing. The index (3) ranges between close to zero (high level of knowledge), when firm is very large and old, and one (low level of knowledge), if it had only one employee and one year old in our case, since we have imposed a censoring level of 20 employees, the maximum value is As discussed in Section 2, three kinds of advantages of the proximity for economic agents (agglomeration economies) can be distinguished: localization, urbanization and competition economies. The localization (or sectoral specialization) economies are measured, for each firm, as the share of other employees working in the same industry (at the two-digit level) within a region (Combes (2000)): 92 LOC jr it jr t r t L Lit L L it, (4) jr r L jr with t L i J it L r and t L i I it jr r, where J and I are the set of firms belonging to industry j in region r and whole region r, respectively, in year t. The urbanization (or sectoral diversity) economies are proxied by the inverse of the Herfindahl- Hirschman index of industry concentration based on the employment share of the different industries (at the two-digit level), except the respective industry j, in a region (Henderson et al. (1995); Combes (2000)): URB jr t 1 HR jr jr t, (5) gr r jr 2 HRt with r L g j g G t Lt Lt r, where G is the set of industries in region r. The measure of industrial diversity (5) ranges between 1 (minimum value), when all other manufacturing employment in the region is concentrated in a single industry, and J 1 (maximum value) if it is uniformly distributed across all (other) industries. As pointed out by Combes (2000), the value of this indicator is not directly linked with the previous one of industrial specialization. In fact, if the regional employment is highly concentrated in a given industry and the several remaining industries have r Since we subtract ith firm s employment, LOC are firm specific.

161 approximately the same size, the value of both indexes (concentration and diversity)for this industry are high. To measure the degree of competition inside each industry at local level (competition externalities), we use the inverse of the Herfindahl-Hirschman index of regional employment concentration: COMP with jr t HJ 1 HJ jr t jr t i J, (6) jr jr L L 2 it t distribution of employment across firms, the lower is the number of firms.. The higher is the employment share of firm i, therefore lesser uniform jr COMP t. The index also tends to increase with Taking into account the theories of innovation and technological diffusion outlined in Section 2, we consider two kind of factors through which regional innovative environment might impact on firm s productivity: knowledge transfer and knowledge base. Some economic agents such as those that operating in KIBS play a crucial role in disseminating knowledge through the region and supporting firms innovative activity. We represent the capacity of transfer knowledge as the number of employees working in KIBS sector in the region. 93 In order to capture the effect of knowledge base, we distinguish two sources: regional R&D employment (RD) and the number of higher degree establishments in a region (UNIV) the role of universities in innovation has been highlighted by various studies, such as Fritsch and Slavtchev (2007) and Cassia et al. (2009) Estimation We adopt the so-called two-step approach. We firstly estimate the factor elasticity parameters of following (log) Cobb-Douglas production function for each two-digit industry, 160 y it a k j it j lit j m it u it (7) where lower-case letters denotes the log upper-case variables of equation (1), to compute firm-level (log) total factor productivity: aˆ it y it j ˆ k it ˆ j lit j ˆ mit. (8) In the estimation of equation (1), we control for macroeconomic shocks by including year dummy variables. Additionally, we assume u it = it + it, with it denoting a firm-specific unobserved component and it a residual term uncorrelated with input choices. Ordinary least-squares (OLS) estimation of equation (7) produces inconsistent estimates due to the likely presence of simultaneity and selection bias: the simultaneity bias arises because input demands are also determined by firm s knowledge of its productivity level, which makes it correlated with the observed inputs; the selection bias is generated by endogenous exit, as smaller firms, with lower capital intensity, are more likely to exit. Assuming that it is time invariant, equation (7) can be estimated using the least square dummy variable approach or the within transformation. 94 Consistency of the fixed effect model requires, however, strictly exogeneity of the included regressors, a non-realistic assumption (Grilliches and Mairesse, 1998). To overcome this problem, we estimate the equation (7) using the generalized method of moments (GMM) methodology for 20 separate industries (at 2-digit level). In particular, we employ the Arellano and Bond (1991) one-step difference GMM (GMM-DIF) estimator, which transforms the panel data model in first differences to remove the individual effects and then 93 According to European Monitoring Centre on Change, KIBS comprises the following CAE-rev2.1 divisions: (CAE 72) computer and related activities, (CAE 73) research and experimental development and (CAE 74) other business activities. 94 The random effects model is rejected in favour of the presence of fixed effects by both Hausman and robust Hausman tests at the 1% significance level (see Wooldrige (2002)).

162 uses lagged levels of the dependent variable and the predetermined variables as instruments for the endogenous differences. 95 We then estimate (in the log form) the model (2): a jr jr jr r r it 0 1 fknowit 1loc it 2urbt 3compt 1kibs t 2univt 3 rd r t it, (9) where the residual term is given by it = i + it. We cannot disentangle firm and regional fixed effects with this formulation, but that does not affect the estimation. Since all covariates are expressed in logarithms, the estimated coefficients can be interpreted as elasticity parameters. Regarding equation (9), we note that it is subject to two main sources of endogeneity: unobserved heterogeneity and simultaneity bias. In fact, some region characteristics (e.g. public infrastructures, local climate, natural resources, etc.) that are not taken into account in this econometric model can affect the propensity to agglomerate, while at the same time agglomeration influences these region characteristics in other words, it is correlated with the independent variables. Additionally, selfselection of the more productive firms also creates a simultaneity problem. Higher productivity in larger markets (or denser areas) may not be due to agglomeration economies (learning effect); it might instead be due to the fact that high-productivity firms are more likely to be attracted to these advantageous markets (selection effect). 96 In other words, because more productive firms are likely located in larger/denser regions, average firm productivity in these regions should be higher even if there are negligible agglomeration economies, which means that OLS estimates might be biased (Baldwin and Okubo (2006); Melitz and Ottaviano (2008); Andersson and Lööf (2009); Saito and Gopinath (2009)). To deal with the endogeneity problem, we estimate the model using again the GMM-DIF procedure. Industry and region dummies were also included in the estimation. As discussed in Section 2, it can be expected that the role of local environment can be different across firms of different sizes. In order to investigate this, we will split the sample into three size classes: firms with , and 251 or more employees (small, medium and largefirms, respectively). The thresholds are those used by the OECD, except for large firms in Portugal, there are only a few firms with more than 500 employees, the OECD threshold Summary statistics Tables 1 and 2 report the summary statistics and the correlations matrix, respectively, of the main variables used in our estimations. Most variables exhibit strong variability, as shown by the large values of standard deviations respective to their mean (Table 1). Even if between variations account for a large part of this heterogeneity, within standard-deviation has a non-negligible role on its explanation. The mean manufacturing firm in the estimation sample has 122 employees and produce 9,812 thousand euros. The correlation matrix reveals that, as expected, there is a statistically significant (at 5%) and negative correlation between TFP and FKNOW recall that lower values of variable mean higher level of knowledge and a statistically significant and positive correlation between TFP and both spatial agglomeration and regional knowledge covariates, except in the case of URB (Table 2). The correlation between the regional knowledge covariates (i.e. KIBS, RD and UNIV) is rather high, which should cause multicollinearity problems in the regressions. Given that, the two explanatory variables that measures the knowledge input available in the region, RD and UNIV, are replaced by their product (i.e. RKNOW = RD UNIV). Figure 1adisplays the distribution of sample firms across the 28 NUTS3 regions. The map shows a high concentration of firms in the North, mainly in the regions of Grande Porto and Ave, but also in Regressions were performed using the Stata, xtabond2 procedure (Roodman, 2009). The results presented in the paper are robust to fixed-effects, Olley and Pakes (1996), Levinsohn and Petrin (2003) and GMM-System methods. These results are available from the authors upon request. 96 In the Portuguese case, larger markets and denser areas are highly correlated.

164 (a) Number of firms (% of total) Figure 1. Number of firms and TFP by NUT3 regions (b) Total Factor Productivity (quintiles) 4. How large are the local environment effects across size classes? The key results of GMM-DIF estimation of model (9) are presented in Table 3 the factor elasticity estimates for each industry, used in the second-step to compute firm-level TFP, are in Appendix Table A1. Column (1) of Table 3 summarizes the main coefficient estimates for the overall sample, while columns (2)-(4) show the results by size classes. The validity of GMM-DIF estimates depends on the absence of second-order serial autocorrelation and on the choice of the appropriate set of instruments. This is indeed the case, since, as expected, the Arellano-Bond AR(1) test shows a negative first-order serial correlation, while the AR(2) test indicates that residuals are seemingly free from second-order serial correlation. Moreover, the null hypothesis of the Hansen test that the overall instruments are valid is not rejected in all four regressions. We note that the Hansen and Sargan tests for over-identifying restrictions show opposite results; however, the Sargan test should be interpreted with care, since the model allows for heteroskedasticity rendering the test baseless. Table 3. Results of GMM-DIF regression Firm size Overall Small Medium Large Variable (1) (2) (3) (4) FKNOW ** *** *** (0.0271) (0.0470) (0.0474) (0.0352) LOC *** * ** (0.0025) (0.0038) (0.0042) (0.0036) URB *** *** ** (0.0191) (0.0248) (0.0341) (0.0361) COMP (0.0066) (0.0091) (0.0096) (0.0131) KIBS ** ** *** (0.0033) (0.0050) (0.0055) (0.0064) RKNOW *** *** ** *** (0.0041) (0.0054) (0.0061) (0.0111) 163 No. of observations 11,015 5,368 3,958 2,107 No. of firms 2,922 1,827 1, No. of instruments

165 AR (1) Prob>z AR (2) Prob>z Sargan test Hansen test Notes: The table summarizes the key coefficient estimates for four different regressions of model (9). GMM-DIF denotes the Arellano-Bond one-step difference GMM estimator. All regressions include industry and region dummies. Variables are in logarithmic form (except in the case of the dummy variables). Robust standard errors are given in parentheses. ***, **, and * denote statistical significance at the.01,.05, and.10 levels, respectively Overall sample analysis Looking at the estimated parameters in column (1) of Table 3, firm s stock of knowledge (FKNOW) has a statistically significant (at 5%) and virtual impact on firm s productivity an increase in knowledge implies that the corresponding index reduces, then increasing the productivity, but it is far to explain all productivity gains. Localization (LOC) and urbanization (URB) economies also positively impact (at the 1% significance level) on the firm s productivity, while no effects of the degree of local competition (COMP) is found at conventional significance levels. In particular, increasing by 1% the share of other employees working in the same industry-region, ceteris paribus, increases the TFP of a firm by %. In the case of the employment share of the other industries in the region, the corresponding increment in the TFP is %. These results seem to point out a superiority of sectoral diversity (urbanization) economies. For its part, regional knowledge also seems to play a key role on firms TFP gains. In fact, both the number of employees working in KIBS sector-region and regional knowledge base have a positive impact (significance at 5% and 1%, respectively) on the productivity increasing KIBS (RKNOW) by 1%, all else equal, increases the TFP by (0.0241)% Differences across firms size We now refine our analysis splitting the sample into three size classes small, medium and large firms, respectively, columns (2), (3) and (4) of Table 3, in order to investigate if there is a difference in the role of the local environment across firms size. Surprisingly, while firm s internal knowledge has a significant (at 1%) expected effect on the productivity level of medium and large firms, it does not seem to impact on the productivity of small firms. A possible explanation for this unexpected finding can be that sample partition created a homogeneous group of (small) firms which have not yet accumulated enough internal knowledge to impact on productivity. Looking at the estimated parameters of agglomeration economies, our first finding is that the effect on productivity level of small and medium firms is higher when the employment in neighbouring firms of the same industry is also higher (relative to total regional employment), while at the same time large firms do not benefit from this sectoral specialization. A second finding is that there is a significant and positive relationship between sectoral diversity and productivity for small and large firms. Finally, the impact of regional knowledge (KIBS and RKNOW) seems to be higher for large firms than small ones CONCLUSION This study focuses on the extent to which the local environment has an impact on productivityacross firms size, using an unbalanced panel of Portuguese manufacturing firms covering the period We assume that both agglomeration economies and regional knowledge have a positive

186 KNOWLEDGE SPILLOVERS AND ECONOMIC PERFORMANCE OF FIRMS LOCATED IN DEPRESSED AREAS: DOES GEOGRAPHICAL PROXIMITY MATTER? Liliana Araújo 1, Sandra T. Silva 2, Aurora A.C. Teixeira 3 1 Faculdade de Economia, Universidade do Porto. 2 CEF.UP, Faculdade de Economia, Universidade do Porto. 3 CEF.UP, Faculdade de Economia, Universidade do Porto; INESC Porto, OBEGEF. Abstract. Extensive literature on the contribution of knowledge spillovers to growth and development at the regional level exists but these studies mainly features regions characterised by a high level of economic development. This paper assesses the importance of knowledge spillovers for firms located in relatively small, peripheral and economically depressed areas. Based on both primary (direct surveys to 257 firms) and secondary data, we concluded that the more relevant knowledge spillovers for firms located in a depressed region of northern Portugal (Vale do Ave) are inter-regional and international. This suggests that the contacts established with sources of knowledge from outside the region under analysis and abroad are crucial for the performance of firms. Despite the innovative intra-industry environment impacts positively on the economic performance of firms, our results convey that in such peripheral and depressed region geographical proximity is not critical for the firms economic performance. JEL-codes: R11, B52, D80, O31, O32 Key-words: Depressed areas; evolutionary economic geography; knowledge spillovers, innovation 1. Introduction Knowledge is defined by several authors, in line with Polanyi (1958), as a learning process that involves cognitive structures and the assimilation of different types of information. A new concept emerges from the diffusion of knowledge: knowledge spillovers. These correspond to a transmission mechanism by which firms benefit from the knowledge produced by other organizations (Sena, 2004). Specifically, knowledge spillovers enable firms to use a greater range of external knowledge, which influences their ability to innovate (Webster, 2004; Yang et al., 2010). Relating knowledge spillovers with innovation activities is crucial and the evolutionary approach to economic geography appears as essential to frame this relationship as it focuses on the importance of organizational routines within the firm on innovation processes (Boschma and Frenken, 2006). The importance of knowledge spillovers has been the focus of a significant number of studies that intend to assess, among other aspects, their contribution to regional growth and to explain the differences in economic performance of firms located in distinct regions (Funke and Niebuhr, 2005; Döring and Schnellenbach, 2006; Rodriguez-Pose and Crescenzi, 2008). These studies emphasize, in particular, the positive impact that knowledge spillovers - from the same region or neighbouring regions - have on the regions growth (Rodriguez-Pose and Crescenzi, 2008). The analysis of the geographical reach of knowledge spillovers is central to a large part of these studies. For some (e.g., Bode, 2004; Verspagen and Schoenmakers, 2004), knowledge diffuses only over short distances, while others (e.g., Bathelt et al., 2004, Teixeira et al., 2008) show that geography is not as relevant in terms of proximity, meaning the transmission of knowledge can occur over long distances. Notwithstanding the valuable contributions to the literature, most studies on knowledge spillovers refer to countries and regions with relatively high levels of development - USA (e.g., Jaffe, 1986), or, in the European context, Germany (e.g., Beise and Stahl, 1999; Bode, 2004; Funke and Niebuhr, 2005), Sweden (Andersson and Karlsson, 2007) and Italy (Caragliu and Del Bo, 2011). Moreover, they use innovation variables such as patents to measure knowledge spillovers, which correspond to only a part of the innovation process of organizations, especially those larger in size and resources, mostly 185

187 located in relatively dynamic and developed areas. The few studies (e.g., Fitjar and Rodríguez-Pose, 2011) that have measured knowledge spillovers employing other variables, such as product and process innovation within firms, and that recognize the importance of using different sources of knowledge more related to the routines of firms in innovation processes, have also focused on more developed countries. More peripheral countries, such as Portugal, have only very recently become the object of interest in terms of research. In particular, Faria and Lima (2012) and Natario et al. (2012) found that the variables measuring innovation and spillovers are positively related to firm performance (Faria and Lima, 2012) and the geographical scope of spillovers in peripheral regions is local, regional and national (Natario et al., 2012). It would be important to also assess within peripheral countries, whether and to what extent the positive effect of knowledge spillovers found in the (few) existing studies occurs in economically depressed areas/regions. The present study, using the concept of depressed areas (PRASD, 2004), analyses the importance and mechanisms of knowledge spillovers to the economic dynamics of firms located in these areas (in this case, the Vale do Ave, a region of Northern Portugal) in a relatively peripheral country such as Portugal (Fontes, 2005). The paper is organized as follows. Section 2 presents a literature review that systematizes the main contributions produced on the concept of knowledge spillovers, within the framework of economic geography and, more specifically, the Evolutionary Economic Geography research line. An overview of the empirical studies that have been submitted in this field is also presented. Section 3 describes the methodology adopted in this study and the empirical results are detailed in Section 4. Finally, we present the conclusions and the main lines for future research Importance of knowledge spillovers for the economic performance of firms 2.1. Concept of knowledge spillovers The complex and nonlinear nature of knowledge has been emphasized by authors such as Polanyi (1958), for whom knowledge is a process that involves cognitive structures that assimilate the information and place it in a broader context, combining a learning process. In his turn, Plotkin (1994) regards knowledge as a dynamic picture in which information can be stored, processed and assimilated, assuming a relational characteristic. Dohse (2001) also believes that knowledge depends on the time and context in which it is generated, it is not static but dynamic and its evolution depends on the institutional framework. Concerning the types of knowledge, Polanyi (1967) distinguishes between explicit and tacit knowledge. According to this author, explicit or codified knowledge is a kind of organized knowledge and it is formalized by certain documents, such as publications. On the other hand, tacit knowledge is embedded in individuals and corresponds to innate values as their own skills. It is a kind of knowledge that develops in individuals through their skills, practices and actions, so it is difficult to transmit. Concepts such as learning-by-doing (Arrow, 1962) and learning-by-using (Rosenberg, 1982) are key elements in the acquisition of tacit knowledge. Know-how is the more difficult form of tacit knowledge for individuals to assimilate and it is not directly or easily transmissible (Cohen and Levinthal, 1990). Knowledge can be incorporated in patents (Greunz, 2005), in machinery and equipment, and can be apprehended through experience, research, observation and learning (Malecki, 2010). The concept of knowledge spillovers has been emphasized by several authors (e.g., Griliches, 1992; Audretsch and Feldman, 2004). These spillovers constitute a process by which firms benefit from the knowledge of other organizations (Sena, 2004), corresponding to a flow of knowledge from a source firm to a beneficiary firm (Griliches, 1992). Knowledge spillovers are still defined as an externality that results from the diffusion of a new technology, product or production process between spatial units such as firms, cities, regions and countries (Caragliu and Del Bo, 2011), and may result from

188 failures in the available mechanisms to protect knowledge generated in innovative firms (Kaiser, 2002). Thus, the process of the creation and diffusion of knowledge has two types of effects: a direct effect on firms that produce knowledge and an indirect effect, related to the possibility of knowledge being absorbed by other firms (Adams and Jaffe, 1996). There are different theories that discuss knowledge spillovers. Knowledge is seen as a major factor explaining the growth differences between regions (Döring and Schnellenbach, 2006), particularly the differences that exist in terms of corporate performance (Faria and Lima, 2012). In this context, the productivity of a firm tends to depend on knowledge spillovers, including the knowledge that such a firm can absorb (Ornaghi, 2006). In the evolutionary economics framework (e.g., Dosi, 1988), knowledge is considered as intrinsically dynamic, it has a cumulative and path dependence nature, and is not transmitted automatically. A key question that emerges from the analysis of knowledge spillovers is its link with innovation. In fact, knowledge spillovers influence the innovativeness of firms, because they can draw on a wider range of external knowledge to conduct innovative activities (Yang et al., 2010). The knowledge diffuses from a source firm to another, when the receptor firm uses this knowledge in innovation activities (Griliches, 1992). Webster (2004) confirms this link between knowledge spillovers and innovation. Through an analysis of large Australian firms, the author measures the reasons that lead firms to engage in innovation activities. The results indicate that the routines common to all industries and knowledge spillovers influence the innovative capacity of firms. Thus, knowledge spillovers seem to be a critical part of the activity and innovative capacity of firms (Czarnitzki and Kraft, 2012). Knowledge spillovers can be categorized into intra-industry spillovers, occurring between firms from the same industry, and inter-industry spillovers that occur between firms in different industries (Steurs, 1995; Kaiser, 2002). In fact, knowledge is not equally accessible to all firms (Jaffe, 1986). According to Henderson and Cockburn (1996), a firm can more easily exploit the knowledge from firms in the same industry (intra-industry spillovers) rather than from that developed in other industries (inter-industry spillovers). Knowledge spillovers do not occur only inside a given region, but can spread to other regions, so the origin of spillovers may be local but may arise outside of the study area too (Audretsch and Feldman, 2004). In this respect, there is a distinction between intra-regional spillovers, which occur within the geographical boundaries of a spatial unit, and inter-regional spillovers, which come from neighbouring regions, highlighting the important role that geography plays in the creation and dissemination of knowledge (Rodriguez-Pose and Crescenzi, 2008). Additionally, depending on the location of the producing agents and receivers of knowledge, there are national and international spillovers. Such spillovers have been emphasized by authors like Harabi (1997) and Negassi (2009). However, there is controversy associated with the geographical scope of spillovers. According to several authors (e.g., Bode, 2004; Verspagen and Schoenmakers, 2004), these occur more frequently among geographically close agents, considering the problem of the transmission of tacit knowledge. On the other hand, other authors (e.g., Bathelt et al., 2004; Teixeira et al., 2008) emphasize that spillovers can occur in geographically distant locations. In this context, Bathelt et al. (2004) distinguish between the concepts of local buzz and pipelines. The buzz is related to short geographical distances that facilitate the generation and dissemination of tacit knowledge, contrary to the definition of pipelines that correspond to the diffusion of knowledge over large geographical distances. Hence the question that arises is how can geography facilitate the dissemination of knowledge spillovers. According to Howells (2002), the importance of geography for knowledge spillovers depends on the type of knowledge and the context of its creation and transfer. Explicit knowledge can be transferred across large distances and at lower costs, whereas the transfer of tacit knowledge involves direct interaction and thus broad spatial proximity (Anselin et al., 1997). 187

189 Importance of knowledge spillovers for the dynamics of firms and regions The studies that analyze the importance of knowledge spillovers for the dynamics of firms and regions focus, in general, on the impact of such spillovers on indicators of the economic performance of firms and regions, for example, the growth rate of GDP per capita of the region (e.g., Rodriguez- Pose and Crescenzi, 2008); and innovation, such as the number of innovations introduced through public research (Beise and Stahl, 1999), or the number of patents granted (Bode, 2004). These studies analyze the dynamics of firms and regions by including variables as measures of knowledge spillovers (Table 1), such as patents of neighbouring regions (Bode, 2004), R&D (e.g., Rodriguez-Pose and Crescenzi, 2008) or accessibility (Andersson and Karlsson, 2007). Referring to inter-regional spillovers, Bode (2004), Funke and Niebuhr (2005) and Rodríguez-Pose and Crescenzi (2008) analyze regions, in particular regions in Germany, in the case of the first two studies, and the European Union in the study by Rodríguez-Pose and Crescenzi (2008). Considering a distinct unit of analysis, Andersson and Karlsson (2007) study the importance of knowledge spillovers in German cities. For the German regions, Bode (2004) examines the impact of knowledge spillovers on the number of patents granted to commercial firms in these regions, while Funke and Niebuhr (2005) evaluate their impact on the region s growth measured by Gross Value Added per worker. Rodríguez-Pose and Crescenzi (2008) also analyze the impact of spillovers on the growth of EU regions, in this case measured by the growth rate of GDP per capita in the region. These three studies yield similar conclusions with regard to proximity, showing that the effects of spillovers decline with distance. Bode (2004) considers two different measures, weighted by distance, for knowledge spillovers: patents and employment in R&D of neighbouring regions. The author concludes that the regions benefit from being near innovative regions with a higher number of patents because the intensity of spillovers is larger. However, the effect of spillovers decreases when the region s innovative capacity rises. Funke and Niebuhr (2005) also found that regional growth is characterized by a spatial pattern because spillovers diminish with distance. This study analyses the effect of knowledge spillovers, measured by the effect of the region s potential R&D on regional growth and concludes that this variable has a positive impact for the German regions. Rodríguez-Pose and Crescenzi (2008) reach the same conclusion, in their analysis of the EU regions (NUTS 1 and 2), using as a proxy for knowledge spillovers the accessibility to innovative activities undertaken in neighbouring regions, weighted by R&D. For this purpose, they develop an accessibility index to innovation that seeks to measure the potential of innovative activity that is carried out in neighbouring regions, representing the accessibility of a region to an activity in another region. Given the cost of knowledge transmission, they calculate the interregional distances in terms of the minutes required to travel through the two regions. The authors conclude that spillovers decrease with distance, but they have a positive effect on regional growth measured by the growth rate of GDP per capita in the region. However, Andersson and Karlsson (2007), in a study on the municipalities in Sweden, conclude that the impact of interregional accessibility (captured through three types of knowledge measured at the municipal level - business R&D, R&D in universities and patents) is not statistically significant on the growth of the municipality, measured by the respective gross product. It should be noted that this same study develops the analysis at both the intra-regional and intra-municipal level and concludes that spillovers have a positive and statistically significant impact on the growth of the city. These four studies focus their analysis on the impact of inter-regional spillovers, using R&D, accessibility and proximity as variables to measure spillovers. The first three (Bode, 2004; Funke and Niebuhr, 2005; Rodríguez-Pose and Crescenzi, 2008) conclude that the effects of spillovers diminish with distance, whereas in the last study (Andersson and Karlsson, 2007), no clear-cut conclusion is reached regarding this matter.

190 Studies on intra-regional spillovers focus mostly on EU regions (Rodriguez-Pose and Crescenzi, 2008) and on municipalities in Sweden (Andersson and Karlsson, 2007). There are however some studies that focus on U.S. firms (Jaffe, 1986) and Germany (Beise and Stahl, 1999), analyzing the effect of knowledge spillovers on different variables. More specifically, Andersson and Karlsson (2007) and Rodriguez-Pose and Crescenzi (2008) analyze the effect of knowledge spillovers on regional growth, while Jaffe (1986) and Stahl and Beise (1999) analyze the effect of spillovers on the number of patents and innovations, respectively. 189 Thus, Rodríguez-Pose and Crescenzi (2008), based on an analysis of EU 25 regions, consider the regional investment in R&D a proxy of local innovative effort, representing the intra-regional

191 190 spillovers. The expenditure on R&D as a percentage of GDP in the region helps to explain the differences in growth across EU regions, so this variable has a positive impact on the regional growth rate of GDP per capita. Andersson and Karlsson (2007) obtain the same conclusion regarding the effect of intra-regional knowledge spillovers. In their study on German cities, the authors found that intra-regional accessibility is positive and statistically significant in the three types of knowledge considered (business R&D, R&D in universities and stock of patents). This means that the effect of knowledge spillovers on the city s growth is limited to existing resources in the region. Regarding the intra-regional spillovers, the issue of the spatial dependence of patents is crucial to highlight the presence of knowledge spillovers, which means it can be applied at firm level. Focusing on the U.S., Jaffe (1986) measured the spillovers through proximity between firms and expenditure on R&D from other firms and found that there was a positive impact of spillovers on these firms patenting activity. The study by Beise and Stahl (1999), aimed at determining whether spillovers have an impact on the number of innovations introduced by firms through access to public research, also include variables such as R&D and proximity, in this case, to research at public institutions. They consider as measures of the spillovers, spending on R&D, firm size and proximity to public research institutions as identified by firms. The authors conclude that, in the case of Germany, firm size and expenditure on R&D have a positive impact, but no significant impact is derived from the proximity of businesses to public research institutions, contrarily to findings from other studies on intra-regional accessibility to R&D in universities, for example, in Andersson and Karlsson (2007). This latter study examines another type of spillovers, the intra-municipal, using three measures of knowledge, business R&D, R&D in universities and patents, where accessibility to knowledge is measured between areas belong to each municipality. As in the case of intra-regional spillovers, intra-municipal accessibility emerges as statistically significant and positive in the three types of knowledge, so its effect on municipality growth is positive, as measured by the variation in added value per employee of the municipality. In summary, these studies show the importance of knowledge spillovers as a determinant of economic performance (e.g., Rodriguez-Pose and Crescenzi, 2008) and innovation (Beise and Stahl, 1999). The studies analyzed employ variables that reflect the formal part of business innovation, embodied in indicators such as patents or R&D. Such activities, however, have little expression in less developed regions, particularly in depressed regions. In this context (see Table 2), Fitjar and Rodríguez-Pose (2011) analyzed 436 firms in a peripheral region of southwest Norway, using the innovation capacity of firms as the dependent variable, measured through the development of new or significantly improved products or by the fact that the innovation is new to the firm or the market. These authors conclude that the development of innovations is positively and significantly influenced by the diversity of information sources used by businesses. They also conclude that sources used by regional and national firms do not significantly influence the development of innovations. In contrast, international sources emerge as significantly and positively related to innovation. In this study, geographical proximity does not seem to be relevant, whereas other types of proximity, such as cognitive and organizational proximity seem crucial. This result is to some extent challenged by Natario et al. (2012), who conclude, by analyzing the innovation process of small and medium enterprises (SMEs) located in the peripheral areas of Guarda, São Miguel and Santa Maria in the Azores, that the level of cooperation between firms and knowledge sources is higher in innovative firms, presenting a more local, regional or national scope of activity rather than international. 98 The knowledge sources are also important in Bonte (2008). This author analyses the dissemination of knowledge and geographical proximity (for a maximum driving time of 2 hours) on the level of trust 98 Similarly to Fitjar and Rodríguez-Pose (2011), aspects related to innovation and spillovers are captured through the number of employees involved in innovation activities, the level of collaboration (low, medium, or high) and the scope of activity (local, regional, national or international).

192 in customer-supplier relationships in 179 aeronautical firms in Germany. Therefore, as in previous studies, this study analyses the importance of access to external knowledge (customers and suppliers) for the achievement of product and process innovations. The appropriability of knowledge in also analyzed, understood as the risk of generated knowledge in the firm to move on to other firms. The level of trust in firms tends to be higher when partners have been important sources of external knowledge in the past. Moreover, trust is hampered when firms are not able to protect their innovations. Bonte (2008) also concludes that geographical proximity has a positive impact on cooperation and confidence level. Referring to the different types of innovation, Czarnitzki and Kraft (2012) employ a sample of 920 innovative firms in Germany, with explanatory variables of firm performance, measured by profit margin, related to knowledge flows that were essential to the development of a product or process, in particular from competitors, customers, suppliers and research institutions. The authors conclude that spillovers from competitors have a positive impact on profits, unlike spillovers from customers, suppliers or research institutions which did not reveal any effect on the firms performance. In the 191

193 case of Faria and Lima (2012) and Ornaghi (2006), despite having concluded that innovation and spillovers are positively related to business performance, the impact of externalities differ in the type of innovation - for the first authors, firms assimilate more knowledge from process innovations than product innovations (although the difference is small), whereas for Ornaghi (2006), product innovations have a relatively higher technological diffusion than process innovations. 3. Methodology 3.1 The selection of the depressed municipalities of Vale do Ave Vale do Ave is located in the Minho region of northern Portugal and is composed of eight municipalities: Fafe, Guimarães, Póvoa de Lanhoso, Santo Tirso, Trofa, Vieira do Minho, Vila Nova de Famalicão and Vizela. This area is classified as depressed (according to Resolução do Conselho de Ministros nº11/2004), based on its local Purchasing Power Index, lower than 75% of the national Purchasing Power Index. The municipalities of Vila Nova de Famalicão, Santo Tirso, Guimarães and Trofa have a Purchasing Power Index of, respectively, 82.4%, 80.4%, 79.8% and 79.5%, exceeding the national average of 75%, so they are not considered depressed municipalities. In contrast, the municipalities of Vizela, Fafe, Póvoa de Lanhoso and Vieira do Minho (with a Purchasing Power Index of 65.6%, 64.8%, 58.4% and 55.4%, respectively), are considered depressed, and businesses located in these municipalities are the target of our analysis (Figure 1). Figure 1: Purchasing Power Index by municipality in Vale do Ave, 2009 Source: Authors compilation based on data from INE (Portuguese Statistics Institute) (2011a). According to the division of firms by sector of activity (cf. data from INE, 2011b), retail and wholesale is the sector with the greatest number of firms in the municipalities of Vale do Ave in general (27.4%) and in the depressed municipalities in particular. Manufacturing is also very relevant in terms of number of firms (16.2%), particularly in the municipalities of Fafe (18.9%) and Vizela (20.3%), emerging with the second highest weight after the trade sector. 192 For a country of intermediate development and it less developed regions, the dynamics of its tradable goods sector and industrial competitiveness are very important. Thus, we decided, in line with several recent studies (e.g., Saito and Gopinath, 2011; Poldahl, 2012), to restrict our analysis of knowledge spillovers in the manufacturing sector. The manufacturing industry in the depressed municipalities of Vale do Ave is mainly comprised of firms belonging to the textile and clothing industries (see Table A1 in Appendix). For the municipalities of Fafe, Póvoa de Lanhoso, Vieira do Minho and Vizela, the weight of these sectors in the total manufacturing industry is 71.2%, 44.0%, 34.0% and 63.6%, respectively, showing their significant importance for these municipalities.

194 The turnover per firm in Vale do Ave is thousands of euros, below the average for Portugal (316.6 thousands of euros). The municipalities in question all have a turnover below the average of Vale do Ave. There is a high percentage of individual firms in Vale do Ave, but below the Portuguese national average. All the depressed municipalities have a proportion of individual firms equal or higher than the average of Vale do Ave. Firms in Portugal, especially in Vale do Ave, are predominantly small, as evidenced by the proportion of firms that have less than 10 workers. However, it is noteworthy that regarding the number of individuals employed by the firm, the average for Vale do Ave (4.2) is higher than the national average (3.5) and the average for the North. Only the municipality of Vizela presents a higher figure (4.7) when compared to the average for Vale do Ave. Employees with a low level of education predominate in all municipalities (below the 3rd cycle of basic education). Employees with higher levels of education (Undergraduate, Master and PhD) have limited expression in the total workforce of Vale do Ave, representing 7% of the total in this region compared to 13% for Portugal as a whole. It should also be noted that in all the depressed municipalities of Vale do Ave this percentage does not exceed 5%. 3.2 Data collection and sample representativeness The process of data collection was based on a survey applied to firms in the depressed municipalities of Vale do Ave. 99 The survey is divided into three parts. In the first part (A), firm data for the years was requested in order to characterize the sample in terms of economic activity, the municipality to which the firm belongs, sales, Gross Value Added and total number of employees, as well as their disaggregation by Undergraduate, Master and PhD qualifications. The second part of the survey (part B) intended to assess the types of innovation (product and process) implemented by firms, including the collaborations that they perform in terms of introducing these innovations. In order to measure the activities related to obtaining and producing new knowledge, some questions for the year 2008 were made about the amounts spent on R&D activities, acquisition of machinery, equipment and software and other external knowledge. The last part of the survey (part C) aimed to assess the contacts that firms establish with different sources of knowledge (e.g., customers, suppliers, universities) in terms of their importance to the firm and their location. The addresses of the firms in the municipalities under analysis were collected between 12 th and 20 th March In order to infer the viability of the survey questions, pilot tests were conducted in that same week, with three firms in the Vale do Ave region, not included in the municipalities under analysis. Given the success of these tests, application of the survey began on 21 st March The questionnaire was sent as a Word file to firms in the municipalities under analysis from an address created exclusively for the purposes of this research. Given that some firms did not have access to , the survey was also sent by fax. The implementation of the survey was closed on 21 st May 2012, although several direct contacts with the firms 100 were required over this period. Based upon the distribution of firms by sector in manufacturing industries, a simple quota sampling was established, in order to ensure the sample s representativeness not only by municipality, but also by sector. The initial goal was to obtain a representative sample of 300 firms in the four municipalities under review, which corresponds to 20.6% of the total manufacturing enterprises in these municipalities, distributed as follows (see Table A2 in Appendix): 165, 50, 10 and 75 for the municipalities of Fafe, Póvoa de Lanhoso, Vieira do Minho and Vizela, respectively. 99 In terms of survey questions, we tried to follow as closely as possible the templates of the Community Innovation Survey (CIS). Although it is possible, by protocol, to access data from CIS 2008, this latest document is not representative at the municipal level, or even NUTS III, including a very small number of firms from the municipalities considered. Thus, there was no alternative but to directly survey the firms located in these municipalities. 100 Many telephone contacts were not valid, so it was not possible to contact those firms. Moreover, some of the firms were already insolvent and others were not willing to respond to our survey. Others also had to be excluded from the analysis because they did not have activity for the period under study. These firms were replaced by others in order to maintain the sample representativeness by sector in each municipality. 193

195 Given the difficulties encountered during the survey process, we only obtained 259 responses from firms located in the municipalities considered (average response rate of 86.3%), which were distributed by municipality in number (response rate) as: 132 (80.0%) in Fafe, 45 (90.0%) in Póvoa de Lanhoso, 8 (80.0%) in Vieira do Minho and 74 (98.7%) in Vizela. 3.3 The specification of theoretical model Based on the literature review developed in Section 2, the theoretical relationship between the economic performance of firms and knowledge spillovers, controlling for a set of factors/determinants that, according to this same literature, will tend to influence performance, is put forward as follows: Loca on of knowledge sources (municipality, Vale do Ave except municipality, outside Vale do Ave, outside the country), Companies that innovate in product or process by industry in Vale do Ave now edge spi o ers Importance of contacts with knowledge sources : Universi es, suppliers, consultants and private ins tu ons of Research and Development ontacts Performance of the company i =f Innova on index (implementa on of product and process innova ons) mp ementa on of inno a on ac i es Collabora ons in the ac vi es of product and process innova on o a ora ons Internal and external Research and Development, acquisi on of machinery, equipment and so ware and other external knowledge taining and prod c on of now edge Industry, total number of employees, human capital ompan characteris cs Purchasing Power Index nicipa it characteris c The first group of determinants corresponds to knowledge spillovers. The location of knowledge sources (e.g., suppliers, universities) was used as a proxy for knowledge spillovers (see Table 3), based on Fitjar and Rodríguez-Pose (2011) and Natario et al. (2012). We developed an index that corresponds to the number of sources that the firm has from each location (municipality, Vale do Ave except the municipality, outside Vale do Ave and outside the country) in order to assess the importance of proximity of knowledge sources for corporate performance. In addition to knowledge sources, we used another variable as a proxy for knowledge spillovers, the intra-industry pool of knowledge and innovation, which corresponds to the ratio of firms that innovate in product or process by sector in Vale do Ave, based on data from 2006 to 2008, from CIS. This variable is in line with the procedures followed in the study by Faria and Lima (2012). The group of contacts corresponds to the level of importance attributed to the information and knowledge sources (Bonte, 2008; Fitjar and Rodríguez-Pose, 2011, Czamitzki and Kraft, 2012). We recoded those variables in order to obtain an index of contacts with universities, suppliers, consultants and private R&D institutions, which includes the use and importance attributed to a particular source. 194 Our model also incorporates the implementation of innovation activities, particularly product and process, in line with Ornaghi (2006) and Faria and Lima (2012). Regarding the implementation of product and process innovations, we developed an index, an Innovation Index, which is the sum of the different types of innovations implemented by firms (e.g., new or improved goods, new or improved services). Moreover, at the level of collaboration in the development of product and process innovations (Fitjar and Rodríguez-Pose, 2011), we recoded the variables in order to determine whether these have an impact on corporate performance.

196 The activities of producing and obtaining knowledge (Ornaghi, 2006; Czamitzki and Kraft, 2012), in particular internal R&D, external R&D, acquisition of machinery, equipment and software, and other external knowledge, have been relativized by the sales value. Finally, we added control variables for the characteristics of firms and municipalities. The group of firm characteristics includes those related with the industry/sector to which the firm belongs and human capital, including the weight of employees with Undergraduate, Master or PhD qualifications in the total number of employees, intended to measure whether the education level of employees influences the performance of firms. We also added a variable to our model that corresponds to a characteristic of the municipalities, the local Purchasing Power Index, which indicates each municipality s level of economic development. Table 3: Model variables and proxies Group of variables Variables Proxies Studies Performance Knowledge spillovers Contacts Knowledge sources Intra-industry pool of knowledge and innovation Implementation of innovation activities Collaborations Obtaining and production of knowledge Firm characteristics Performance of the firm (average for the Gross Value Added per worker in ln years ) Localization of knowledge sources (municipality, Vale do Ave except Number of sources from each location - municipality, outside Vale do Ave, Index 0 to 8 in ln outside the country) Firms that innovate in product or process by industry in Vale do Ave Importance of contacts with knowledge sources Universities, suppliers, consultants and private institutions of R&D Innovation Index (implementation of product and process innovations - goods, services, methods of manufacture or production, methods of logistics, delivery or distribution, activities to support processes) Collaborations in the activities of product and process innovation Internal R&D External R&D Acquisition of machinery, equipment and software Acquisition of other external knowledge Industry Total number of employees (average for ) Human capital (average for ) Ratio of firms that innovate in product or process by industry in Vale do Ave Development of a composite variable with the following variables: - Utilization (dummy variable: 0 if no, 1 if yes) - Importance (1 if none, low, medium, 2 if high) Importance index from 0 to 2 (Utilization x Importance) in ln Innovation Index - sum of implemented innovations by the firm - index of 0 to 5 (0 means no innovation and 5 means that the firm implements all the different types of innovation) in ln Collaborations in product innovation (0 if there is no collaboration, 1 if it is primarily the firm or group, 2 if it is the firm in cooperation or primarily other firms or institutions) in ln Collaborations in process innovation (0 if there is no collaboration, 1 if it is primarily the firm or group, 2 if it is the firm in cooperation or primarily other firms or institutions) in ln Ratio of Internal R&D by sales Ratio of External R&D by sales Ratio of acquisition of machinery, equipment and software by sales Ratio of acquisition of other external knowledge by sales Industry: dummy variable (0 if it is other industries, 1 if it is clothing and textile industries) Total number of employees in ln Ratio of number of employees with Undergraduate/Master/PhD in the total number of employees Faria and Lima (2012) Fitjar and Rodríguez-Pose (2011), Natário et al. (2012) Faria and Lima (2012) Bönte (2008), Fitjar and Rodríguez- Pose (2011), Czamitzki and Kraft (2012) Ornaghi (2006), Faria and Lima (2012) Fitjar and Rodríguez-Pose (2011) Ornaghi (2006), Czamitzki and Kraft (2012) Natário et al. (2012) Fitjar and Rodríguez-Pose (2011) Faria and Lima (2012) 195

197 Municipality characteristic Purchasing Power Index Purchasing Power Index in ln Source: Own elaboration. Rodriguez-Pose and Crescenzi (2008) Empirical results 4.1. Descriptive analysis There is a concentration of firms around relatively low values of sales per employee, with an average of euros/year. The same applies to Gross Value Added per employee, a measure of firm performance used in our econometric model, whose average value stands at euros/year. The clothing and textile industries have a high weight in the municipalities under analysis, so we joined these two categories into a single industry, while the remaining cases are treated together (other industries). In the total of the four municipalities (see Table A3 in Appendix), 64.5% of the firms belong to the clothing and textile industries. In the municipalities of Fafe and Vizela, the percentage of firms in this industry is high (72.7% and 63.5%, respectively). The importance of this industry is much lower in Vieira do Minho ( only 25.0%), when compared to the other municipalities. Regarding the human capital of firms located in the municipalities studied, we found that the percentage of employees with higher education (Undergraduate, Master or PhD) is relatively low - on average, only 3.2% of employees in enterprises located in these municipalities has an undergraduate degree or higher, being particularly low in Vieira do Minho (1.1%). The weight of employees engaged in R&D is even lower for the municipalities as a whole (0.8%). In relation to types of innovations, we found that approximately half (51.0%) of the firms stated they have implemented innovations to support process activities, where innovation in manufacture or production methods is the most significant (47.9%). On average, only 19.3% of the firms innovate in logistics, delivery or distribution. Concerning the collaboration of firms by type of innovation, we classified the five types of innovation mentioned above into two major groups, product innovation and process innovation. In all the municipalities, there is a small percentage of firms that collaborate with other firms or institutions, where the implementation of innovations is carried out primarily by the firm or the group to which it belongs. In fact, in the four municipalities, the development of product innovations is mainly conducted by the firm or the group to which it belongs (83.4%) and a low percentage by the firm in cooperation with other firms or institutions (16.0%), with only 0.6% occurring in other enterprises or institutions. The same is true for the development of process innovations, because only 13.2% of the firms cooperate with other firms or institutions, whereas 5.4% of the firms indicate that these innovations are mainly developed by other firms or institutions. Finally, it appears that firms in all the municipalities spend more on the purchase of machinery, equipment and software. In fact, the weight of this spending in sales is 5.7% for all municipalities, although Fafe and Póvoa de Lanhoso present a slightly higher figure (6.6%). In contrast, the acquisition of other external knowledge has little or no relevance in these municipalities. The main information and knowledge sources for innovation activities used by firms in these municipalities are (see Table A4 in Appendix): the firm itself, customers and suppliers. In contrast, the least used information sources are universities or other higher education institutions and state laboratories or other public organizations with R&D activities. Specifically, about 70% of the surveyed firms use their own firm (or group of firms) and customers to obtain information/knowledge, and in these cases, more than 80% consider these sources as highly relevant to their activities - these two groups shared a pole position on the most relevant source of information for the surveyed firms. The external sources of information that require some pro-activity and organizational complexity by firms e.g., universities and other higher education institutions - are barely used (only 4.6% of the

198 surveyed firms admit to their use, and from these, only half consider this source as very important for the development of their activities, with only 8.3% regarding it as the most important source). In addition to resources and importance of knowledge sources, it is critical to assess the respective location (Table A5 in Appendix), especially if they belong to the municipality in analysis or to Vale do Ave except the municipality, outside Vale do Ave or outside the country. This feature allows us to assess the potential existence of geographical knowledge spillovers. According to the data collected, almost all the firms and groups of firms that constitute an information and knowledge source relevant to the firm s innovation activities during the period , are located in the same municipality of the reference firm (the surveyed firm). Similarly, but to a lesser extent, competitors or other firms in the same industry are located near the firm of reference - or in the municipality or in other municipalities of Vale do Ave. Customers, suppliers and professional and business associations seen as relevant to the firm s innovation activities are located primarily in Vale do Ave (except the municipality where the firm is located) or outside Vale do Ave. Universities or other higher education institutions and state laboratories or other public institutions with R&D activities that are relevant to the activities of the firms analyzed are located mainly outside Vale do Ave, but inside the country. Foreign entities seem to be considered important only for a minority of firms, involving clients/consumers (36.5%), equipment suppliers and related (16.9%), consultants, laboratories or private R&D institutions (10.0%) and competitors or other firms in the same industry (6.1%). These data seem to indicate that the surveyed firms generally fail to perform prospection and to use information and knowledge sources for innovation outside their restricted scope of location and/or action. Based on a correlation analysis (Table A6 in Appendix), in terms of knowledge spillovers, sources located at greater geographical distance from the firm (outside Vale do Ave and outside the country), reveal a closer relation with the firm s economic performance. In other words, on average, firms that recognize that the most critical information and knowledge sources for their innovation activities are located outside Vale do Ave and especially outside the country, are those that have higher levels of productivity. Still regarding spillovers, the importance of firms in the same sector of activity that innovate in product or process in Vale do Ave also seems to be related to the highest economic benefits for the firms themselves; we found particularly that a larger number of firms in the same industry in Vale do Ave that innovate tend, on average, to be correlated with better firm performance. Furthermore, the use of universities and other higher education institutions and suppliers as sources of knowledge for innovation activities emerge as positively associated with better performance in firms of these depressed municipalities of Vale do Ave. We also found that innovation (in its different dimensions) appears associated with good economic performance. Thus, a higher number of different types of innovation (e.g., new goods, new services) are associated with higher economic performance, measured by Gross Value Added per worker. Additionally, collaboration with other firms or institutions in the development of product and process innovations appears correlated with firms that have higher productivity. In relation to firm characteristics, we observed a negative correlation of Gross Value Added per worker with industry. This means that firms belonging to the textile and clothing industry have, on average, lower productivity than their counterparts in other industries. We also concluded that firm size (total number of employees) and, in particular, the respective human capital (weight of employees with higher education in total number of employees) is positively correlated with firm performance, showing that larger firms with higher levels of qualification/education are more productive. Finally, regarding correlations between independent variables, it is crucial to note that there is a strong correlation between the Innovation Index and collaboration in terms of the development of 197

199 product and process innovations, such as between the location of sources in the municipality itself and Vale do Ave (except the municipality itself). Also, there is a high correlation between the industry dummy variable and the ratio of firms that innovate in product or process by industry in Vale do Ave Results of the econometric estimation Given the strong correlation between the Innovation Index and the proxy variables of collaboration in the development of product and process innovations, as well as between the location of sources of knowledge and information for innovation activities from the firm s municipality and in Vale do Ave except the municipality, we decided it would be appropriate to estimate four different models (see Table 4). Thus, Model 1 includes collaborations in terms of product and process innovations and Model 2 considers the innovation index. These two models comprise the knowledge sources located in Vale do Ave except the municipality. Models 3 and 4 are similar to Models 1 and 2 but include knowledge sources located in the firm s municipality rather than in Vale do Ave except the municipality. The estimation (via method of ordinary least squares) of the four models (see Table 4), produced similar results, with an acceptable quality of adjustment (cf. adjusted R 2 ). Taking into account standard significance levels (1%, 5% and 10%), we found that knowledge spillovers associated with geography, measured in this study by the location of sources of knowledge, emerge with huge importance. Thus, knowledge sources located in the firm s municipality (Models 3 and 4) and located in Vale do Ave except the municipality are statistically significant and negative. Firms that have a greater number of sources of knowledge for innovation activities located in their municipality or Vale do Ave except their municipality tend to observe, ceteris paribus, a lower level of productivity. In contrast, firms with a greater number of knowledge sources for innovation activities outside Vale do Ave, and especially abroad, emerge as, ceteris paribus, more productive. Table 4: Results of the econometric estimation Group of variables Proxies Model 1 Model 2 Model 3 Model 4 Knowledge Spillovers Contacts (index in ln) Implementation innovation activities Collaborations Number of knowledge sources (in ln) located Pool of knowledge and innovation of Obtaining and production of knowledge in the municipality ** -0,435 ** in Vale do Ave except the municipality *** *** outside Vale do Ave * * ,342 * outside the country * * ** 0,491 ** Ratio of firms that innovate in product or process by industry in Vale do Ave * ** * 1,187 * Importance of Universities 0, Importance of suppliers 0, Importance of consultants and private R&D institutions -0, Innovation Index (in ln) 0,423 *** *** in product innovation (in ln) in process innovation (in ln) Ratio of internal R&D by sales -0, Ratio of external R&D by sales 10, Therefore, due to a potential problem of multicollinearity, it is not advisable to put these two variables in the same model. We decided to exclude the industry dummy variable from the results presented in the main text and keep the variable used as a proxy for spillovers in our econometric estimation. The results of econometric estimation without the variable pool of knowledge and innovation and with the industry variable can be consulted in the Appendix (see Tables A7-A8) and did not differ widely from the results presented in this section.

200 Ratio of acquisition of machinery, equipment and -0, software by sales Ratio of acquisition of other external knowledge by sales 10, Dimension (total number of employees in ln) 0,216 *** *** *** *** Human Capital (ratio of the Firm characteristics number of employees with higher education in the total 3,154 ** ** *** *** number of employees) Municipality characteristic Development level (Purchasing Power Index in ln) -0, Constant 7, N Adjusted R 2 0, Note: statistically significant at: * 10%, ** 5%, *** 1%. In this context, inter-regional knowledge spillovers and particularly international ones emerge as crucial for firms located in depressed regions, in particular, those located in the relatively underdeveloped municipalities of Vale do Ave. Still on the spillovers proxy, associated with the pool of knowledge and innovation of firms in the region belonging to the same industry, it emerges as a key determinant of productivity. In other words, the intra-industry innovation environment associated with firms located in the Vale do Ave region influences positively and significantly the firm s performance. To that extent, the presence of innovative firms in the same industry seems to be critical for firms located in depressed areas it is here that the importance of intra-industry spillovers resides. Notwithstanding, the most innovative firms (cf. Models 1 and 3) present, ceteris paribus, higher levels of productivity. Firms that show higher levels of collaboration for innovation (product and process) did not emerge as significantly different from other firms. This result is most probably associated with the fact that the main key issue here is not so much the collaboration type, as we measured, but its frequency. We concluded, finally, that firm size (total number of employees) and the respective human capital (weight of employees with higher education) are positive and statistically significant variables in the four models. Thus, ceteris paribus, on average, larger firms and with higher levels of human capital emerge as more productive (with higher levels of Gross Value Added per worker). 5 Conclusions This study analyzed the importance of knowledge spillovers for the economic performance of firms in a region in Northern Portugal, Vale do Ave, focusing particularly on four municipalities in the region - Fafe, Póvoa de Lanhoso, Vieira do Minho and Vizela - identified as economically depressed areas. We concluded that geographical proximity does not seem to be an important aspect for the performance of firms in these municipalities. According to the results, municipal and intra-regional knowledge spillovers lead to lower performance levels in the firms of these municipalities. In contrast, contacts with knowledge sources outside Vale do Ave (inter-regional spillovers) and outside the country (international spillovers) appeared as significant for the performance of these firms, having a positive and significant effect. This finding seems to contrast with the results conveyed by the studies that focus on more developed areas, including regions of Germany (Funke and Niebuhr, 2005) and the EU (Rodríguez-Pose and Crescenzi, 2008), which concluded that the effects of spillovers on the growth of these regions diminish with distance. Regarding the studies on firms located in peripheral areas, Natario et al. (2012) concluded, contrary to our study, that the scope of activity of these firms is local, regional or national, rather than 199

201 international. However, Fitjar and Rodríguez-Pose (2011), who analyzed firms in peripheral areas of Norway, show that international sources play a key role in the innovation performance of firms. In this latter case, geographical proximity does not emerge as particularly important, being the organizational and cognitive proximity critical to business innovation. In the same line, Amin and Cohendat (2000) reported that relative proximity to organizational routines is more important than geographical proximity. Our study, concluding that inter-regional and international spillovers are crucial for firms in these depressed municipalities of Vale do Ave, seems to show, in line with recent studies, that geographical proximity is not the critical dimension of the firms economic performance. This result suggests that other types of proximity beyond the geographical are more important for these firms, such as routines in organizational innovation, as Boschma (2005) pointed out. Still in relation to knowledge spillovers, the innovations developed by other firms also emerged as relevant to the performance of a given firm. We found that the intra-industry innovative environment in Vale do Ave, identified by the pool of knowledge and innovation (measured by the relative weight of firms that innovate in product or process in Vale do Ave by industry), significantly influences the economic performance of a particular firm in these municipalities. These results are in line with Faria and Lima (2012), who demonstrated that, also for a number of Portuguese firms, the percentage of firms involved in innovation activities influences the performance of other firms. In relation to aspects associated with innovation, the most innovative firms, which implement different types of product and process innovation, are also associated with a higher performance. With respect to the characteristics of firms, the results show that the employees qualifications are also important, and that firms with a higher percentage of skilled employees have a better performance. Additionally, innovation activities developed in these firms, as well as the contacts made with inter-regional and international sources of knowledge in the years from 2006 to 2008, were found to positively influenced the firm s performance, as measured by Gross Value Added per employee in the triennium from 2009 to This evidence confirms the relevance of the phenomenon of path dependency, as supported by evolutionary economic geography (Boschma and Frenken, 2006). In terms of economic policy, the results highlight that it is crucial to promote policies that encourage businesses, particularly in depressed areas, to develop innovations, but especially given the positive effect of inter-regional and international spillovers, to establish contacts with sources from other regions, particularly at the international level. These international contacts are relevant to the economic performance of a firm, in particular the contacts established with clients, suppliers of equipment, materials, components or software and consultants, laboratories or private R&D institutions, which correspond to the most used knowledge and information sources for innovation activities at international level by the firms in these depressed areas. 200 References Adams, J. and Jaffe, A. (1996), Bounding the effects of R&D: an investigation using matched establishment firm data, The RAND Journal of Economics, Vol. 27, pp Amin, A. and Cohendat, P. (2000), Organisational learning and governance through embedded practices, Journal of Management and Governance, Vol. 4, pp Andersson, M. and Karlsson, C. (2007), Knowledge in regional economic growth - the role of knowledge accessibility, Industry and Innovation, Vol. 14, pp Anselin, L., Varga, A. and Acs, J. (1997), Local geographic spillovers between university research and high technology innovations, Journal of Urban Economics, Vol. 42, pp Arrow, K. (1962), The economic implications of learning by doing, Review of Economic Studies, Vol. 29, pp

205 Manufacture of fabricated metal products, except machinery and equipment Manufacture of computer, communication equipment, electronic and optical products Manufacture of machinery and equipment n.e.c. Manufacture of furniture Other manufacturing activities Repair, maintenance and installation of machinery and equipment Total Source: Own elaboration. 204 Table A3: Values of relevant variables in the model, by municipality and overall (%) Póvoa de Vieira do Total of the Fafe Vizela Lanhoso Minho four (N=132) (N=74) (N=45) (N=8) municipalities Firms that belong to textile and clothing industry Ratio of employees with higher education in total number of employees Ratio of employees engaged in R&D in total number of employees Firms that innovate in goods Firms that innovate in services Firms that innovate in manufacturing or production methods Firms that innovate in logistics, delivery or distribution methods Firms that innovate in support activities to processes Collaboration in product innovations: Primarily the firm or the group to which it belongs Firm in cooperation with other firms or institutions Primarily other enterprises or institutions Collaboration in process innovations: Primarily the firm or the group to which it belongs Firm in cooperation with other firms or institutions Primarily other enterprises or institutions Ratio of R&D activities undertaken inside the firm by sales Ratio of acquisition of external R&D by sales Ratio of acquisition of machinery, equipment and software by sales Ratio of acquisition of other external knowledge by sales

206 Ta e A 4: Use and importance of now edge so rces for the firm s inno ation acti ities (for new innovation projects or for their conclusion) in the period from 2006 to 2008 Firms that Firms that Firms that attribute classify the use the high source in the source importance first place (%) to the source (%) (%) Inside the firm or other firms in the same group Suppliers of equipment, materials, components or software Clients or customers Competitors or other firms in the same sector of activity Consultants, laboratories or private R&D institutions Universities and other institutions of higher education State laboratories or other public institutions with R&D activities Professional or business associations Conferences, fairs, exhibitions Scientific journals and technical, professional and commercial publications Ta e A5: Location of now edge and information so rces for the firm s inno ation acti ities (for new innovation projects or for their conclusion) in the period from 2006 to 2008 Location Vale do Ave Outside Outside the Municipality except Vale do country (%) municipality Ave (%) (%) (%) Inside the firm or other firms in the same group 98,9 1,1 0,5 0,0 Suppliers of equipment, materials, components or software 29,7 65,5 62,8 16,9 Clients or customers 39,2 61,3 59,1 36,5 Competitors or other firms in the same sector of activity 66,7 63,6 25,8 6,1 Consultants, laboratories or private institutions of R&D 12,0 60,0 38,0 10,0 Universities and other institutions of higher education 8,3 33,3 58,3 0,0 State laboratories or other public institutions with activities of R&D 33,3 33,3 66,7 0,0 Professional or business associations 18,8 40,0 48,6 0,0 Note: Each firm surveyed could choose more than one answer option, hence percentages do not add up to 100%. 205

210 AN EXPLORATORY SPATIAL ANALYSIS ABOUT THE SPATIAL DISTRIBUTION OF ECONOMIC ACTIVITIES IN PORTUGAL Pedro Monteiro 1, Miguel Viegas 2 1 Universidade de Aveiro (DEGEI), Campus Universitário de Santiago, Aveiro, PORTUGAL. 2 GOVCOPP, Universidade de Aveiro (DEGEI), Campus Universitário de Santiago, Aveiro, PORTUGAL. Abstract. Economic activities are not distributed evenly throughout the territory. As such, the geographical concentration of economic activities has aroused a great interest in the academic community, following many famous examples of Silicon Valley (California), Route 128 (Boston), Cambridge (UK), the federal state of Baden Wurttemberg (Germany), among others. Since the early contributions of (Thunen, 1826) about the location of agricultural activities around the preindustrial city, many authors have been seeking to describe the factors that determine the distribution of economic activities in the territory. In the early 90s, Michael Porter carried out on behalf of the Portuguese government, a study on the Portuguese economy which identified six priority industry clusters in traditional sectors: wine, tourism, automobile, footwear, textiles, wood and cork (M. Porter, 1994). In 2001, the thematic of clusters was recalled, through the government initiative PROINOVA - Integrated Program to Support Innovation, designed to support the development of innovation clusters in key areas. Currently, the program COMPETE - Operational Program Thematic Factors of Competitiveness ( ) mentions within its Collective Efficiency Strategy the existence of poles of competitiveness and technology and other clusters such as energy, health or agro industrial (Compete, 2009). Given the importance of this matter, taken as a priority in terms of economic development policies, the aim of this paper is to measure and describe the spatial distribution pattern of the main sectors of economic activity in Portugal. For this we follow the methodology of (Guillain & Le Gallo, 2007), combining the locational Gini index with an Exploratory Spatial Data Analysis, applied to the employment data by sector and by municipalities in 2009 and This approach has the advantage of introducing a spatial dimension to the usual measures of concentration, thus seeking to determine the location pattern of each sector of activity and measure spatial correlation (Guillain & Le Gallo, 2007). Keywords: Agglomeration, Exploratory Spatial Data Analysis, Locational Gini index, Portuguese municipalities 1. INTRODUCTION Economic activities are not distributed evenly throughout the territory. Be that at regional, national or intercontinental level, the history of human civilizations shows that communities, through extensive migrations have concentrated increasingly on certain areas which currently represent a small proportion of the total surface of the planet. According to Eurostat data and the latest survey LUCAS (Land Use Cover Area frame Survey, March 2011), the areas designated for residential, commercial and industrial purposes, including infrastructures, occupy only 11% of the total area of Europe at 27 (except Bulgaria, Cyprus, Malta and Romania). As it can be seen in Figure 1, these occupancy rates vary considerably from region to region (NUTS II), with variation of up to 90% in Inner London or below 5% in Iberian Peninsula or northern Europe. The soil, with its environmental, productive and supportive functions, assumes a central role in ecosystems and biodiversity conservation and is a fundamental resource for economic activities. The distribution of different land uses is influenced by numerous biological, geographical and socioeconomic factors and largely determines their occupation through a constant and mutual interaction. The geographical concentration of economic activities has aroused a great interest in the academic community, following many famous examples like Silicon Valley (California), Route 128 (Boston), Cambridge (UK), the federal state of Baden Wurttemberg (Germany), among others. Since the early 209

211 contributions of (Thunen, 1826) about the location of agricultural activities around the preindustrial city, many authors have been seeking to describe the factors that determine the distribution of economic activities across the territory. Alfred Marshall opposing the Fordism production model describes an alternative model called the industrial district. The industrial district is defined as a production system, geographically limited, and based on an intense division of labor between small and medium sized enterprises within the same industrial sector (Marshall, 1919). According to Paul Krugman, considered the father of the New Economic Geography, agglomeration of firms in a restricted area of the territory arises from the interaction between economies of scale, transport costs and the difference in labor costs between sectors ("Home Market Effect ") in a circular process with positive feedback effects ((Krugman, 1980), (Krugman, 1991)). The cluster concept, another expression for economic agglomeration popularized by the work of Michael Porter, can be defined as a network of interdependent companies and institutions, geographically close to each other and linked together through trades, technologies and common know-how (M. E. Porter, 1998). In the early 90s, Michael Porter carried out on behalf of the Portuguese government, a study on the Portuguese economy which identified seven priority industry clusters in traditional sectors: wine, tourism, automobile, footwear, textiles, wood and cork (M. Porter, 1994). In 2001, the thematic of clusters was recalled, through the government initiative PROINOVA - Integrated Program to Support Innovation, designed to support the development of innovation clusters in key areas (Choringas, 2009). In this context, the program identified seven mega clusters: food, habitat, fashion, leisure, mobility, health and personal services, and information and entertainment, and three clusters: footwear, automobile and Software (Choringas, 2009). Like the Porter report, also PROINOV was abandoned prematurely. Currently, the program COMPETE - Operational Program Thematic Factors of Competitiveness ( ) mentions within its Collective Efficiency Strategy the existence of poles of competitiveness and technology and other clusters such as energy, health or agro industrial (Compete, 2009). Given the importance of this matter, taken as a priority in terms of economic development policies, the aim of this paper is to measure and describe the spatial distribution pattern of the main sectors of economic activity in Portugal. For this we follow the methodology of (Guillain & Le Gallo, 2007), combining the locational Gini index with an Exploratory Spatial Data Analysis, applied to the employment data by sector and by municipalities in 2009 and This approach has the advantage of introducing a spatial dimension to the usual measures of concentration, thus seeking to determine the location pattern of each sector of activity and measure spatial correlation (Guillain & Le Gallo, 2007). Our paper is divided as follows: in the next section, we describe the data and methodology used to estimate the pattern of concentration and location of different sectors of economic activity. The main results are presented in the third section and section 4 concludes with some final comments. 210

212 Figure 1 2. METHODOLOGY AND DATA DESCRIPTION It is not easy, nor is there consensus on the methodology to measure or assess empirically the effects of clustering of economic activities. 102 In this article, we seek to combine concentration measurements with the new tools of spatial econometrics, based on the methodology followed in (Guillain & Le Gallo, 2010). As a measure of concentration we use the locational Gini Coefficient whose expression is: Where: (1) See for alternative methods: Ellison, G., & Glaeser, E. L. (1997). Geographic concentration in U.S. manufacturing industries: A dartboard approach. Journal of Political Economy, 105,

213 G m,, represents the locational Gini coefficient of economic sector m; n, the numbers of municipalities; And finally, The locational Gini coefficient of a sector assumes a zero value when the distribution of the respective employment is uniform in all the municipalities. If the total employment in a sector of economic activity is concentrated in a single municipality the locational Gini coefficient takes the value 0.5. Although the locational Gini coefficient is a good indicator of the degree of concentration or dispersion of various sectors of economic activity, it tells us nothing, firstly, on the pattern of geographic distribution, and secondly on the specific location of possible clusters. That is, assuming that there is a phenomenon of concentration of handwork of a particular economic sector in some cities, it may be useful to know, first, if there is a specific pattern of distribution within these councils and secondly, considering the existence of agglomeration effects, where are these clusters. Moran's I statistic seeks to answer the first question. It measures the relation between the normalized deviation of a variable at a specific location and the normalized deviation in neighboring geographic units for the same variable. Considering a row-standardized contiguity matrix (type queen) w, the Moran s I statistic is given by: (2) The spatial weight matrix W is a matrix of contiguity matrix in which if i and j are neighbors, otherwise and by convention. The Moran s I Statistic constitutes a measure of spatial autocorrelation for a given attribute, ranging from -1 to 1 like any other correlation index. A Moran s I Statistic close to zero (technically, close to -1 / (n-1)) indicates a random pattern. When above -1/(n-1) (toward +1), it indicates a tendency toward clustering and when below -1 / (n-1) (toward -1) it indicates a tendency toward dispersion. The locational Gini coefficient and the Moran s I Statistic give us valuable indications on the tendency of economic sectors to concentrate and form clusters (Arbia, 2001). However, it tells us nothing about the spatial location of these specific manifestations of agglomeration. Thus, these global indexes if relevant can be an invitation to explore other local measures of agglomeration. The statistical LISA (Local Indicator of Spatial Association) decomposes the Moran s I Statistic in order to identify the individual contribution of each local site (in our case, each municipality). It measures for each geographical unit the spatial autocorrelation of the variable between this unit and all the neighboring units according to the criteria of the spatial weight matrix. The local version of Moran s I Statistic index for each municipality i is given by (Anselin, 1995): (3) 212 Where notation j concerns only the neighboring values of municipalities i. As such, Local Indicators of Spatial Association (LISA) indicate the presence or absence of significant spatial clusters or outliers for each location. A randomization approach is used to generate a spatially random reference distribution to assess statistical significance with 999 permutations. The observation of the position of each municipality in the four quadrants of the Moran Scatterplot for each sector of economic activity allows the distinction of four different categories: Municipality with a high proportion of workforce in the sector m and positive autocorrelation with the neighborhood: type HH (high-high)

214 Municipality with a high proportion of workforce in the sector m and negative autocorrelation with the neighborhood: type HL (high-low) Municipality with low proportion of workforce in the sector m and positive autocorrelation with the neighborhood: type LL (low-low) Municipality with low proportion of workforce in the sector m and negative autocorrelation with the neighborhood: type LH (low-high) The Moran Significance Map, unlike the Moran Map shows only those geographical units where the LISA is significant and identifies each with a color types. As part of our analysis, we are particularly interested in the types HH and HL, the first for her centrifugal dynamics and the second because of its shadow effect on the neighborhood. The study area corresponds to the Portuguese continental territory. For this study we used data of workforce employed in enterprises according to the CAE-Rev.3 (Classification of Economic Activities) available in the Regional Statistical Yearbook of the National Statistics Institute (INE, IP, System Integrated Business Accounts) for the biennium , and disaggregated across the 284 municipalities of mainland Portugal. In a first level of aggregation, we studied the manufactory sector taken as a whole (sector C), the construction sector (sector F) and the tourism sector (sector I). In a second approach, we disaggregate the manufactory sector in various subsectors, namely: footwear (15), textiles and clothing (13 +14), wood, cork and furniture (16 +31), chemical and rubber ( ), metallurgy and basic metal products (24+25), automobile ( ), food and beverages (10 +11) and machinery and equipment ( ). These choices were strongly influenced by the availability of data which forces us to aggregate several related subsectors. 3. RESULTS AND DISCUSSION Based on data for the eleven sectors and subsectors described above, we proceed with a first analysis of the first and more comprehensive measures of concentration and agglomeration, leaving for the second part the analysis of the existence of possible effects of local clusters. Table 1 show for each of the sectors and subsectors the locational Gini index, the Moran s I Statistic and the respective rankings. 103 For the three global sectors (tourism, construction and manufacturing), we found lower concentration, which is natural taking into account its higher degree of aggregation. As for the Moran s I Statistic, we find that it is in the manufacturing sector that agglomeration effects are felt most, followed by tourism and construction. The case of tourism should be interpreted with caution since this tendency for aggregation of municipalities with higher proportion of workforce employed in tourism may have to do only with geographical and climatic factors rather than socio-economic dynamics. As for construction, and like other service sectors oriented to people, the phenomenon of agglomeration reflects mostly the population densities rather than sectorial dynamics. Table 1 Activities Gini Ranking oran Ranking footwear 0, , textiles and clothing 0, , machinery and equipment 0, , automobile 0, , chemical and rubber 0, , wood, cork and furniture 0, , food and beverages 0, ,2385 metallurgy and metal products 0, ,4758 Tourism 0, , Construction 0, , Manufactory sector 0, , All Moran s I Statistic proved highly significant.

215 Looking now to the various sub-sectors of the manufacturing sector, and crossing the locational Gini index and the Moran s I Statistic, we can distinguish four patterns of concentration / agglomeration. Firstly we have the subsectors with high concentration of activities with a strong tendency to aggregate. Fall into this category as textiles and clothing. There are municipalities with a high proportion of workers in this subsector and this concentration tends to spread through other neighboring municipalities. Secondly we have the subsectors with high concentration of activities but with a lower tendency for aggregation. 104 Belong to this group the subsector footwear, automotive and machinery and equipment. In this case, technological factors associated with economies of scale seem to be dominant, despite some sprawl dynamic. Thirdly, we have the sub-sectors of activity less concentrated but with strong tendency to agglomerate represented by a single subsector, metals and metal products. In this pattern, the dynamics of agglomeration between several municipalities supersede the measures of concentration that remain moderate. Finally, there is a rather undefined pattern with low concentration and low tendency to aggregate, in which fall the remaining subsectors of the manufacturing industry: chemical, rubber, wood and cork furniture and food and beverages. Figures 2-9 represent the Moran Significant map and help us to understand better patterns of geographic location. The different types HH, LL, LH and HL appear on maps respectively marked in red, blue, light blue and pink. We have chosen not to display the maps relating to the construction and machinery and equipment because we do not found any clear pattern of clustering. The first map (Figure 2) represents the manufactory sector. It clearly shows a pattern of industrial location in three relatively distinct poles: the first corresponds to the municipalities of Leiria and Marinha Grande, a second corresponding to the district of Aveiro and a third covers a number of municipalities between Porto and Braga, thus covering the regions of Grande Porto and Ave. The second map (Figure 3) corresponds to the tourism sector, with a clear geographic concentration in the south (Algarve) and in the Alentejo coast, motivated, in our view, essentially by climatic factors. The next maps (Figures 4-9) illustrate the spatial distribution of several manufactory subsectors described above. Figure 4 corresponds to the Moran Significance Map of textiles and clothing subsector and identifies two industrial spots. The first one is localized in the north of Porto and comprising the Ave Valley, and part of the Cávado and Minho-Lima regions. Therein lays the stronghold of the Portuguese textile industry. The second spot, in the central region, covers part of the Serra da Estrela, Cova da Beira regions and also includes the Guarda municipality. The first case is not surprising, considering the sector modernization effort that patent in multiple partnerships with various private and public research units in a broad effort in converting and adapting to globalization. 105 The second case invites a better account of existing dynamics. Considering the existence of a spatial agglomeration in the Moran Significance Map, this leads us to believe that the wool sector is eventually resisting despite major closures that have dominated the last decades A small tendency but still significant and positive. 105 The CITEVE - Technological Centre for the Textile and Clothing Industries of Portugal is an establishment localized in Famalicão created in 1986 aims to support the development of technical and technological capacities of textile and clothing, and by fostering the diffusion of innovation, promoting quality improvement and instrumental support for the definition of industrial policies for the sector.

217 FIGURE 6: RUBBER AND CHEMICALS PRODUCTS FIGURE 7: WOOD, CORK AND FURNITURE 216 FIGURE 8: METALLURGY AND METAL PRODUCTS FIGURE 9: AUTOMOBILE INDUSTRY Figure 5 describes the footwear sector and displays also two regional clusters, one located in the north of Portugal covering the municipalities of Vale do Ave (Guimarães, Fafe) and Tâmega (Amarante etc.), and a second one in the region of Entre-Douro-e-Vouga (with the municipalities of Ovar, Feira, S. João da Madeira, Oliveira de Azeméis and Arouca). Without going into details about

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